medical research council scale for muscle strength

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Muscle strength grading.

Usker Naqvi ; Andrew L. Sherman .

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Last Update: August 28, 2023 .

• Introduction

Muscle strength testing is an important component of the physical exam that can reveal information about neurologic deficits. It is used to evaluate weakness and can be effective in differentiating true weakness from imbalance or poor endurance. It may be referred to as motor testing, muscle strength grading, manual muscle testing, or many other synonyms. The muscle strength evaluation may be performed by nurses, physicians, physical therapists, occupational therapists, chiropractors, and other practitioners.

The function of muscle strength testing is to evaluate the complaint of weakness, often when there is a suspected neurologic disease. It is an integral part of the neurologic exam, especially for patients with stroke, brain injury, spinal cord injury, neuropathy, amyotrophic lateral sclerosis, and a host of other neurologic problems.

The most commonly accepted method of evaluating muscle strength is the Medical Research Council Manual Muscle Testing scale. This method involves testing key muscles from the upper and lower extremities against the examiner’s resistance and grading the patient’s strength on a 0 to 5 scale accordingly:

• 0 No muscle activation
• 1 Trace muscle activation, such as a twitch, without achieving full range of motion
• 2 Muscle activation with gravity eliminated, achieving full range of motion
• 3 Muscle activation against gravity, full range of motion
• 4 Muscle activation against some resistance, full range of motion
• 5 Muscle activation against examiner’s full resistance, full range of motion

Commonly tested muscles include the shoulder abductors, elbow flexors, elbow extensors, wrist extensors, finger flexors, hand intrinsics, hip flexors, knee extensors, dorsiflexors, great toe extensor, and plantar flexors. These muscle groups are commonly chosen, so that important spinal nerve roots are assessed systematically; however, further muscles can be tested to evaluate individual peripheral nerves. For example, testing the strength of the elbow flexors, elbow extensors, wrist extensors, finger flexors, and hand intrinsics allow for a methodical evaluation of the C5 to T1 nerve roots. However, one could more specifically test the thumb abductors to evaluate the median nerve and the abductor digiti minimi to evaluate the ulnar nerve.  [1] [2] [3]

• Issues of Concern

Proper technique must be employed during testing to ensure valid results. Tight or restrictive clothing should be removed so that the examiner can visualize the muscles being tested and observe for muscle twitch. The examiner should also stabilize the joint and ensure that other muscles do not provide assistance. Muscles should first be tested with gravity eliminated by positioning the patient, so that muscle contraction is perpendicular to gravity, such as along an examining table or bed. If the patient is unable to engage the muscle with gravity eliminated, the examiner should place a hand on the muscle and ask the patient to contract his or her muscles again. This allows the examiner to feel for a muscle twitch, even if a twitch is not visible. This observation would differentiate a score of 0 from a score of 1. When the patient demonstrates the full range of motion with gravity eliminated, the test should be repeated against gravity for the full range of motion. If this is successful, the patient should be challenged by the addition of a small degree of resistance, then maximal resistance by the examiner. The unaffected or less affected side should be tested first to gauge contralateral strength for comparison; all four limbs should be tested for completeness and to help guide the differential diagnosis based on patterns of weakness, such as upper extremity only, lower extremity only, or proximal muscles rather than distal.  [2]

The Medical Research Council Manual Muscle Testing method is very common, easy to perform, and does not require any specialized equipment. Despite these advantages, it also has its limitations. Scoring is subjective based on the examiner’s perception. There is variability between examiners for the maximal resistance they are able to apply, as some examiners are stronger than others. The test does not account for musculoskeletal conditions that may make testing painful or difficult to tolerate, such as tendinopathy or arthritis. The test is dependent on patient effort, which may be poor in some patients, owing to pain, proper comprehension of instructions, psychological causes, or secondary gain. Finally, the grading system classifies strength level but does not directly quantify strength.  [4]

The Alternatives to the Medical Research Council Manual Muscle Testing system aims to quantify strength directly in terms of pounds, Newtons, or other units. This requires specialized equipment, most commonly dynamometers. Dynamometry provides a more precise measurement of the force that a muscle can exert and can allow for differences in strength to be tracked over time that an examiner may not subjectively notice when using the MRC scale. Hand-grip dynamometry is a popular example, in which the patient squeezes a handle that records the force being applied. Limitations of dynamometry include the need for costly or specialized equipment, limited muscle groups that can be tested, and limited availability of testing equipment to clinicians across specialties or settings.  [5]

Another approach to muscle strength testing involves testing functional movements instead of quantifiable strength. Examples of functional tests include squatting or rising from a chair. Functional strength tests provide information about whether the patient is strong enough to perform essential daily activities, a limitation of both the Medical Research Council Manual Muscle Testing method and dynamometry. However, functional strength tests do not provide a grade or numeric quantity that can be tracked over time to gauge improvement.  [5]

• Clinical Significance

Muscle strength testing can help a practitioner diagnose neurologic problems in which weakness is a prominent deficit. The muscles targeted for testing should be methodically chosen based on suspected diagnoses and for complete characterization of the strength deficit in various limbs. Careful technique is important for ensuring valid and reproducible results. The Medical Research Council Manual Muscle Testing method is commonly accepted, performed across several disciplines, does not require special equipment, and demonstrates reasonable interrater reliability. More precise methods of measurement, such as hand-grip dynamometry, are less subjective and provide a quantifiable measurement that can be tracked over time. Functional assessment of strength focuses on how independently patients are able to perform their activities of daily living and whether strength is a limiting factor.

In patients with fictitious or hysterical weakness, the initial resistance to movement may appear normal, followed by a sudden giving away. Or the individual may not be using the adjacent or other supportive muscles in an appropriate fashion.

• Other Issues

Limitations of the Grading Scale

• Muscle being tested may have no clinical relevance
• There may be individual variation in reporting
• Only assesses muscles which are contracting in a concentric manner
• The scale may not be applicable in all patients
• Enhancing Healthcare Team Outcomes
• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Usker Naqvi declares no relevant financial relationships with ineligible companies.

Disclosure: Andrew Sherman declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Naqvi U, Sherman AL. Muscle Strength Grading. [Updated 2023 Aug 28]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• Variance in manual treatment of nonspecific low back pain between orthomanual physicians, manual therapists, and chiropractors. [J Manipulative Physiol Ther. 2...] Variance in manual treatment of nonspecific low back pain between orthomanual physicians, manual therapists, and chiropractors. van de Veen EA, de Vet HC, Pool JJ, Schuller W, de Zoete A, Bouter LM. J Manipulative Physiol Ther. 2005 Feb; 28(2):108-16.
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MRC Muscle Scale - MRC

The MRC scale for muscle power was first published in 1943 in a document called ‘Aids to the Investigation of Peripheral Nerve Injuries (War Memorandum No. 7)’.  This became a standard text resource which was reprinted many times, and is referred to widely in a number of documents and papers. In the 1970s the document was republished with the title ‘Aids to the Examination of the Peripheral Nervous System (Memorandum No. 45)’.

The muscle scale grades muscle power on a scale of 0 to 5 in relation to the maximum expected for that muscle. In a recent comparison to an analogue scale the MRC scale is more reliable and accurate for clinical assessment in weak muscles (grades 0-3) while an analogue scale is more reliable and accurate for the assessment of stronger muscles (grades 4 and 5).

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The MRC Muscle Scale is licensed under the Open Government Licence .

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Aids to the Examination of the Peripheral Nervous System (Memorandum No. 45) is licensed under the Open Government Licence 3.0.

You must give appropriate credit (“Used with the permission of the Medical Research Council”) and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests that the MRC endorses you or your use.

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MRC Scale | Muscle Strength Grading | Strength Testing

• Assessment E-Book

MRC stands for Medical Research Council which is the institution that sets up the standard for muscle strength testing.

The strength can be categorized on a level from zero to five. In our example, we will use the extension of the knee joint. So, the muscle which we are going to test is the Quadriceps.

The levels are as follows:

• Grade 0: The patient cannot activate the muscle, so no movement is observed. For grade 0, ask the patient to contract his quadriceps. He can do this by pushing the back of his knee into the bench. For grade 0, I will not see or feel a flicker or trace of contraction or movement.
• Grade 1: the patient can activate the muscle, without moving the limb. So only a trace or flicker of movement is seen or felt during palpation of the muscle. For grade 1, ask the patient to do the exact same thing, and this time, you will see or feel a muscle flicker or trace of movement.
• Grade 2: movement over the full range of motion can only occur if gravity is eliminated. In order to distinguish between grades 1 and 2, we have to bring our patient in side-lying position to eliminate gravity. Then, I will support the leg of my patient, bring it into full flexion and ask my patient to move into extension. If my patient is able to move through the full range of motion, this is a grade 2. If no movement is possible at all, we are talking about grade 1.
• Grade 3: the patient can overcome gravity and move through the full range of motion without resistance coming from the examiner. For grade 3, I’ll ask my patient to extend his knee against gravity.
• Grade 4: weakness with resistance. So your patient can move through the full range of motion with moderate resistance coming from the examiner. For grade 4, I will give moderate resistance against the extension of my patient’s knee.
• Grade 5: full strength. So your patient can move through the whole range of motion against full resistance coming from the examiner.And for grade 5, give full resistance against the extension of the patient’s knee. In order to distinguish between a grade 4 and 5, make sure to compare both legs.

Now that you’ve seen the basics of how to test according to the MRC scale, make sure to practice this with different joints and muscles and figure out a way of how to position your patient.

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Understanding Muscle Strength: The Importance of the MRC Scale

The MRC Scale: Understanding Muscle Strength

The Medical Research Council (MRC) Scale is a widely used tool in healthcare settings to assess muscle strength and function. Developed by the Medical Research Council in the UK, this scale provides a standardized method for healthcare professionals to evaluate and document muscle strength in patients.

The MRC Scale consists of six grades, ranging from 0 to 5, each representing a different level of muscle strength:

• Grade 0: No contraction
• Grade 1: Flicker or trace of contraction
• Grade 2: Active movement with gravity eliminated
• Grade 3: Active movement against gravity
• Grade 4: Active movement against some resistance
• Grade 5: Normal strength

Healthcare professionals use the MRC Scale to assess muscle strength in various clinical settings, such as neurology, orthopaedics, and rehabilitation. By assigning a grade to each muscle group tested, clinicians can track changes in muscle strength over time and monitor the effectiveness of treatment interventions.

Understanding the MRC Scale is crucial for accurate assessment and management of conditions affecting muscle strength, such as stroke, spinal cord injury, neuromuscular disorders, and musculoskeletal injuries. By using this standardized scale, healthcare providers can communicate effectively about a patient’s muscle strength status and collaborate on treatment plans.

In conclusion, the MRC Scale is a valuable tool that plays a vital role in assessing and monitoring muscle strength in clinical practice. Its simplicity and reliability make it an essential component of comprehensive patient care across various medical specialties.

Understanding the MRC Scale: Key Questions and Comparisons

What is the mrc 5 point scale, what is mrc vs nyha scale, what does mrc measure, what is the mrc scale, what is the mrc grade of muscle power, what is the mrc copd scale.

The MRC 5-point scale, also known as the Medical Research Council 5-point scale, is a standardized tool used in healthcare to assess muscle strength. This scale consists of six grades, ranging from 0 to 5, with each grade indicating a different level of muscle strength. Grade 0 represents no muscle contraction, while Grade 5 signifies normal strength. Healthcare professionals use this scale to evaluate and document muscle strength in patients across various medical disciplines, enabling them to track changes in muscle function over time and tailor treatment plans accordingly. Understanding the MRC 5-point scale is essential for accurate assessment and management of conditions affecting muscle strength, facilitating effective communication among healthcare providers and ensuring comprehensive patient care.

The frequently asked question about the MRC scale versus the NYHA scale often arises in healthcare discussions regarding the assessment of different aspects of a patient’s health. The MRC scale, developed by the Medical Research Council, is primarily used to evaluate muscle strength and function, providing a standardized grading system from 0 to 5. In contrast, the NYHA (New York Heart Association) scale is specifically designed to assess the functional capacity and limitations of patients with heart failure based on their symptoms and ability to engage in physical activity. While the MRC scale focuses on muscle strength assessment, the NYHA scale concentrates on evaluating cardiovascular function and its impact on daily activities, highlighting the distinct yet complementary roles these scales play in assessing different aspects of a patient’s overall health.

The Medical Research Council (MRC) Scale is a clinical tool used to measure and assess muscle strength in individuals. Specifically, the MRC Scale quantifies the strength of different muscle groups by assigning grades ranging from 0 to 5 based on the level of contraction and resistance during specific movements. This standardized scale helps healthcare professionals evaluate muscle function accurately, track changes in strength over time, and tailor treatment plans for conditions affecting muscle strength, such as neurological disorders, injuries, and rehabilitation cases. Understanding what the MRC Scale measures is essential for healthcare providers to effectively evaluate and manage patients’ muscle strength status in various clinical settings.

The MRC scale, short for the Medical Research Council scale, is a standardized tool used in healthcare to assess and measure muscle strength. This scale consists of six grades, ranging from 0 to 5, each indicating a different level of muscle strength. Healthcare professionals rely on the MRC scale to evaluate patients’ muscle strength in various clinical scenarios, enabling them to track changes over time and tailor treatment plans accordingly. Understanding the MRC scale is essential for accurate assessment and management of conditions affecting muscle strength, facilitating effective communication among healthcare providers and ensuring optimal care for patients.

The MRC grade of muscle power refers to the classification system used by healthcare professionals to assess and quantify muscle strength in patients. The Medical Research Council (MRC) Scale assigns a numerical grade, ranging from 0 to 5, to indicate the level of muscle power exhibited by an individual. This grading system helps clinicians evaluate the extent of muscle weakness or paralysis in specific muscle groups, enabling them to tailor treatment plans and monitor progress effectively. Understanding the MRC grade of muscle power is essential for accurate diagnosis, treatment evaluation, and rehabilitation planning in various medical conditions affecting muscle strength.

The MRC COPD scale, also known as the Medical Research Council Chronic Obstructive Pulmonary Disease scale, is a commonly used tool to assess the impact of breathlessness on individuals with COPD (Chronic Obstructive Pulmonary Disease). This scale ranges from 1 to 5, with higher scores indicating more severe breathlessness and limitations in daily activities. Healthcare professionals often use the MRC COPD scale to evaluate the functional status of patients with COPD, guide treatment decisions, and monitor disease progression over time. Understanding and regularly assessing patients using the MRC COPD scale can help healthcare providers tailor interventions and support individuals in managing their condition effectively.

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• Published: 18 September 2020

Medical Research Council-sumscore: a tool for evaluating muscle weakness in patients with post-intensive care syndrome

• Zeynep Turan   ORCID: orcid.org/0000-0001-8142-3467 1 ,
• Mahir Topaloglu 1 &
• Ozden Ozyemisci Taskiran 1  

Critical Care volume  24 , Article number:  562 ( 2020 ) Cite this article

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Dear Editor,

COVID-19 may lead to severe acute respiratory distress syndrome requiring intensive care unit (ICU) support. Patients surviving respiratory distress could develop post-intensive care syndrome (PICS) that includes ICU-acquired weakness (ICUAW). Nearly 66% of COVID-19 patients have clinically important muscle weakness following discharge [ 1 ]. Therefore, communication between the critical care and rehabilitation physician is important to evaluate the physical function of COVID-19 survivors to start rehabilitation timely.

The comprehensive examination of muscle strength in COVID-19 is not easy. Muscle strength can be evaluated by manual muscle testing and dynamometer. Electrophysiological study is important in diagnosing critical illness neuromyopathy; however, its correlation with muscle weakness is not clear. Ultrasonography can detect atrophy and structural changes but does not correlate with muscle function [ 2 ].

Medical Research Council (MRC)-sumscore evaluates global muscle strength. Manual strength of six muscle groups (shoulder abduction, elbow flexion, wrist extension, hip flexion, knee extension, and ankle dorsiflexion) is evaluated on both sides using MRC scale. Summation of scores gives MRC-sumscore, ranging from 0 to 60. This score was developed for detecting early strength alterations in patients with Guillain-Barré syndrome, especially who were bedridden and receiving artificial ventilation. The sensitivity and interobserver agreement of MRC-sumscore was demonstrated [ 3 ]. Despite its ceiling effect, this score reliably identifies significant weakness (< 48) and even better in severe weakness (< 36) [ 4 ] which is the main medical interest for treatment in ICUAW.

Handgrip strength is a rapid, simple, and objective tool that is measured by handheld dynamometer represents global muscle strength. The cutoff value for handgrip strength in critically ill patients is defined as < 11 kg force for males and < 7 kg force for females which is below that of the age- and sex-matched patients [ 5 ]. It was proposed as an alternative to MRC in ICUAW [ 5 ]. However, examination of other muscles by MRC-sumscore might give additional information since the neurological consequences of COVID-19 are not clear yet. ICUAW is more pronounced in proximal muscles; therefore, direct evaluation of proximal muscles is also valuable. MRC is associated with mortality, hospital, and ICU-free days in ICUAW more strongly than handgrip strength [ 5 ].

In conclusion, MRC-sumscore is a valid, reliable, objective, and easy method to evaluate the global muscle strength including PICS related to COVID-19. It provides beneficial information about the clinical course. Its bedside applicability without necessitating any device makes MRC-sumscore a valuable tool in the follow-up of patients with PICS.

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Not applicable

Abbreviations

Intensive care unit

Intensive care unit acquired weakness

Medical Research Council

Post-intensive care syndrome

Wang Z, Wang Z, Sun R, Wang X, Gu S, Zhang X, et al. Timely rehabilitation for critical patients with COVID-19: another issue should not be ignored. Version 2. Crit Care. 2020;24(1):273. https://doi.org/10.1186/s13054-020-02967-7 .

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Hermans G, Van den Berghe G. Clinical review: intensive care unit acquired weakness. Crit Care. 2015;19(1):274. https://doi.org/10.1186/s13054-015-0993-7 .

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Department of Physical Medicine and Rehabilitation, Koc University School of Medicine, Maltepe Mah, Davutpasa Cad, No:4, Topkapı, Zeytinburnu, 34010, Istanbul, Turkey

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ZT contributed substantially to the conception and design of the study, drafted and provided critical revision of the article, and took responsibility in necessary literature review for the study. MT contributed substantially to the conception of the study and took responsibility in necessary literature review for the study. OOT contributed substantially to the conception and design of the study and drafted and provided critical revision of the article. All authors read and approved the final manuscript.

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Turan, Z., Topaloglu, M. & Ozyemisci Taskiran, O. Medical Research Council-sumscore: a tool for evaluating muscle weakness in patients with post-intensive care syndrome. Crit Care 24 , 562 (2020). https://doi.org/10.1186/s13054-020-03282-x

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DOI : https://doi.org/10.1186/s13054-020-03282-x

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Correspondence to: Ingemar Sergio José Merkies, MD, PhD, Spaarne Hospital Hoofddorp, Spaarnepoort 1, 2134 TM, Hoofddorp, The Netherlands E-mail: [email protected]

*The members of PeriNomS Study Group are provided in the Acknowledgements section.

Corresponding author.

Received 2011 Apr 24; Revised 2011 Sep 18; Accepted 2011 Sep 21; Issue date 2012 May.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0 ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

The Medical Research Council grading system has served through decades for the evaluation of muscle strength and has been recognized as a cardinal feature of daily neurological, rehabilitation and general medicine examination of patients, despite being respectfully criticized due to the unequal width of its response options. No study has systematically examined, through modern psychometric approach, whether physicians are able to properly use the Medical Research Council grades. The objectives of this study were: (i) to investigate physicians’ ability to discriminate among the Medical Research Council categories in patients with different neuromuscular disorders and with various degrees of weakness through thresholds examination using Rasch analysis as a modern psychometric method; (ii) to examine possible factors influencing physicians’ ability to apply the Medical Research Council categories through differential item function analyses; and (iii) to examine whether the widely used Medical Research Council 12 muscles sum score in patients with Guillain–Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy would meet Rasch model's expectations. A total of 1065 patients were included from nine cohorts with the following diseases: Guillain–Barré syndrome ( n  = 480); myotonic dystrophy type-1 ( n  = 169); chronic inflammatory demyelinating polyradiculoneuropathy ( n  = 139); limb-girdle muscular dystrophy ( n  = 105); multifocal motor neuropathy ( n  = 102); Pompe's disease ( n  = 62) and monoclonal gammopathy of undetermined related polyneuropathy ( n  = 8). Medical Research Council data of 72 muscles were collected. Rasch analyses were performed on Medical Research Council data for each cohort separately and after pooling data at the muscle level to increase category frequencies, and on the Medical Research Council sum score in patients with Guillain–Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy. Disordered thresholds were demonstrated in 74–79% of the muscles examined, indicating physicians’ inability to discriminate between most Medical Research Council categories. Factors such as physicians’ experience or illness type did not influence these findings. Thresholds were restored after rescoring the Medical Research Council grades from six to four options (0, paralysis; 1, severe weakness; 2, slight weakness; 3, normal strength). The Medical Research Council sum score acceptably fulfilled Rasch model expectations after rescoring the response options and creating subsets to resolve local dependency and item bias on diagnosis. In conclusion, a modified, Rasch-built four response category Medical Research Council grading system is proposed, resolving clinicians’ inability to differentiate among its original response categories and improving clinical applicability. A modified Medical Research Council sum score at the interval level is presented and is recommended for future studies in Guillain–Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy.

Keywords: MRC, manual muscle testing, Rasch, neuromuscular disorders

Introduction

In 2005, a historical essay tracing the history of scoring and summation of neuromuscular weakness as part of daily neurological practice was published by Dyck et al. (2005) . Mitchell and Lewis (1886) initiated the practice of alphanumerical scoring of neurological signs in the 19th century. However, it was Lovett, an orthopaedic surgeon, who introduced an ordinal scoring of muscle weakness that formed the basis for the Mayo Clinics and Medical Research Council (MRC) manual muscle testing grading systems, of which the MRC system is most widely used ( Medical Research Council, 1943 ; Dyck et al. , 2005 ). Its worldwide recognition is most probably due to its simplicity, and drawings illustrating how limb muscles should be tested. Through the decades, various versions have been published that aimed to improve the methods for muscle examination. The 2010 edition of Aids to the Investigation of Peripheral Nerve Injuries. Medical Research Council: Nerve Injuries Research Committee was recently presented on behalf of the guarantors of Brain , embracing a historical review and appreciation for its nurtures through the decades ( Compston, 2010 ). Despite being the most cardinal feature of daily neurological practice, the MRC scale has been respectfully criticized due to the unequal width of its categories, with Grades 1, 2 and 3 being too narrow, and 4 being too broad, often leading to attempts to modify the scale ( Brandsma et al. , 1995 ; Dyck et al. , 2005 ; Cuthbert and Goodheart, 2007 ; MacAvoy and Green, 2007 ; Merlini, 2010 ).

One of the most common sources of improper use of any outcome measure concerns the inconsistent use of the response options that correspond to the scales’ items ( Tennant and Conaghan, 2007 ). This results in what is known as ‘reversed or disordered thresholds’. The term threshold refers to the point between two adjacent response categories where either response is equally probable. In the case of the MRC scale, a threshold would be the point between two adjacent categories, such as between MRC Grades 2 and 3. Disordered thresholds occur when physicians have difficulty consistently discriminating between the MRC grades in patients with various degrees of muscle weakness. Surprisingly, no study has systematically examined the appropriateness of the MRC scale using modern psychometric techniques.

The objectives of this study were: (i) to examine the applicability and discriminative capacity of physicians using the MRC grades in patients with various neuromuscular illnesses with different degrees of muscle weakness. We questioned whether physicians could demonstrate a fairly uniform MRC grades’ ordered thresholds pattern along the Rasch scale continuum, since previous reports suggested human's inability to differentiate between more than four response options ( Andrich, 1996 ; Penta et al. , 2001 ); (ii) to investigate the influence of factors possibly affecting the proper use of the MRC grades in clinical practice (such as physician's clinical experience). For these two objectives, the Rasch method as a modern psychometric vehicle was used, solely focusing on threshold and item bias examinations ( Rasch, 1960 ; Tennant and Conaghan, 2007 ); and (iii) since Guillain–Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) are potentially treatable illnesses and the MRC sum score has been often used as an outcome measure to determine efficacy in these illnesses, we have chosen to examine whether this multi-item scale would fulfil all Rasch model expectations in patients with Guillain–Barré syndrome and CIDP and if not, to propose changes to improve its use ( Kleyweg et al. , 1991 ; van der Meche and Schmitz, 1992 ; Merkies, 2001 ; van Koningsveld et al. , 2004 ; Hughes et al. , 2008 )

Patients and methods

The MRC grades of various muscles were collected from different neuromuscular seminal studies published in the last two decades. Most of these studies have guided the worldwide neurological community in understanding the clinical and therapeutic pattern of these illnesses. A total of 1065 patients (Guillain–Barré syndrome: n  = 480; myotonic dystrophy type 1: n  = 169; CIDP: n  = 139; limb-girdle muscular dystrophy: n  = 105; multifocal motor neuropathy: n  = 102; Pompe's disease: n  = 62; and monoclonal gammopathy related polyneuropathy of undetermined significance: n  = 8) were included ( Table 1 and Supplementary Table 1 ) ( van der Meché and Schmitz, 1992 ; van der Kooi et al. , 1996 ; de Die-Smulders et al. , 1998 ; Van den Berg-Vos et al. , 2002 ; van Koningsveld et al. , 2004 ; Hagemans et al. , 2005 ; Van Asseldonk et al. , 2005 ; Hughes et al. , 2008 ; Hermans et al. , 2010 ). The initial MRC data of all patients from the above-mentioned cohorts were selected for the purposes of the current study. All patients met their international criteria for their illness ( Asbury and Cornblath, 1990 ; AANA, 1991 ; Bushby and Beckmann, 1995 ; Hirschhorn and Reuser, 2001 ; EFNS/PNS, 2006 ; Prior, 2009 ). The diagnosis ‘monoclonal gammopathy related polyneuropathy of undetermined significance’ was established after excluding all possible causes for the gammopathy and polyneuropathy ( Miescher and Steck, 1996 ). For all studies, consent was obtained according to the Declaration of Helsinki and approval was obtained by the Ethical Committee of the institution in which the original study was performed.

Basic characteristics of patients with neuromuscular disorders

In the INCAT studies, a total of 113 patients were examined (Guillain–Barré syndrome, n  = 83; CIDP, n  = 22 and monoclonal gammopathy-related polyneuropathy of undetermined significance, n  = 8).

ICE CIDP = immune globulin intravenous for CIDP; INCAT = inflammatory neuropathy cause and treatment.

Assessment scale

The MRC grading system provides the following grades: 0, paralysis; 1, only a trace or flicker of muscle contraction is seen or felt; 2, muscle movement is possible with gravity eliminated; 3, muscle movement is possible against gravity; 4, muscle strength is reduced, but movement against resistance is possible and 5, normal strength.

The MRC grades of the following six muscle pairs comprise the MRC sum score for Guillain–Barré syndrome and CIDP: upper arm abductors, elbow flexors, wrist extensors, hip flexors, knee extensors and foot dorsal flexors ( Kleyweg et al. , 1991 ). In the remaining cohorts (monoclonal gammopathy of undetermined significance related polyneuropathy, multifocal motor neuropathy, mytonic dystrophy type-1, Pompe's disease and limb-girdle muscular dystrophy), the muscles groups evaluated represented the clinical picture of each illness (see Supplementary Table 1 for available muscles per cohort).

Rasch analysis

Rationale for using rasch method.

In health care, outcome measures often consist of ordinal multi-item questionnaires, based on the classical test theory ( DeVellis, 2006 ). Concerns have been raised about inappropriate analysis of the generated summed scores that are erroneously assumed to be at the interval level ( Wright, 1999 ; Svensson, 2001 ; DeVellis, 2006 ). The ability of a scale to provide fundamental measurements should be established before the more commonly reported psychometric attributes such as being simple, valid, reliable and responsive ( Tennant et al. , 2004 a ; Tennant and Conaghan, 2007 ). Modern scientific methods have been adopted to overcome the shortcomings of traditional measurements. One of the most widely used modern techniques is the Rasch method that transforms ordinal obtained scores into interval-level variables, and whose fit of data satisfies numerous checkpoints as part of model expectations ( Rasch, 1960 ; Tennant et al. , 2004 a ; Tennant and Conaghan, 2007 ).

In the current study setting, the Rasch model assumes that a patient with less weakness (thus more strength) will have a greater chance of receiving a higher MRC grade by the examining physician. A comprehensive description of the Rasch analysis specifically for neurologists is provided elsewhere ( Pallant and Tennant, 2007 ; Tennant and Conaghan, 2007 ; van Nes et al. , 2011 ). Briefly, the Rasch model shows what should be expected in response to ordinal items if interval scaling is to be achieved. For this, the following criteria should be fulfilled.

Thresholds examination: when using items with more than two response categories, as for the MRC grades, proper ordering of the response options should be verified using category probability curves for each muscle group examined, since this will reflect the ability of physicians to use the MRC in a correct way ( Shaw et al. , 1992 ). Ordered thresholds are where the transition (threshold) between categories map on to the underlying construct in the expected manner. Thus the transition between categories (e.g. 1–2 and 2–3) reflects increasing levels of muscle strength ( Fig. 1 , top). Disordered thresholds can occur when physicians use the response options inconsistently, and this inconsistency can be a source of misfit to model expectations. The difficulty discriminating between response options may be a result of too many options, or where the labelling of the options is confusing, both of which may lead to misinterpretation.

Fit statistics: fit statistics give an indication of how well the items fit the expected ordering required by the model. This ordering is a probabilistic version of Guttman Scaling ( Guttman, 1950 ). There are two general categories for detecting misfit: overall (summary) misfit, using the entire response matrix, and the individual fit (examining all items and all persons individually). At the summary level the overall mean residual values for both persons and items can be calculated. These values are expressed as a z -score with a mean of 0 and a SD of 1, values of which indicate perfect fit to model expectations ( Tennant et al. , 2004 b ; Pallant and Tennant, 2007 ). The summary item–trait interaction statistic reflects the fit of the observed data to the model's expectations and is represented by the chi-square. This statistic gives an indication of the invariance of the ordering of items across patients with different levels of muscle strength. A significant chi-square indicates a failure to retain this ordering. Besides the overall fit residuals, individual item–chi square and item and person residuals can be calculated ( Tennant et al. , 2004 b ; Pallant and Tennant, 2007 ; Vandervelde et al. , 2007 ).

Item bias: response to an item should not vary between groups (e.g. males versus females), given the same level of the underlying trait (e.g. muscle strength). We assessed item bias (differential item functioning) on the MRC data for various available person factors. A panel (I.S.J.M. and C.G.F.) have studied the range of the factors age, disease duration and physician's experience in the available cohorts. Subsequently, these factors were categorized into subgroups for item bias analyses, aiming for an equivalent distribution of participants among the subgroups (25–33% per subgroup).

Local dependency: local dependency arises when items are linked such that the response on one item is dependent upon the response to another. Item sets with correlations >0.3 are considered a source of misfit to the model ( Tennant and Conaghan, 2007 ).

Unidimensionality: the Rasch model assumes unidimensionality and consequently post hoc tests are included in the analysis to ensure that this assumption holds. These tests involve a comparison of person estimates (of muscle strength) based upon two sets of items identified from the first principal component analysis of the residuals. The estimates for every individual are compared by a t -test, and where <5% of these comparisons are significantly different, this is taken to support the assumption of unidimensionality ( Smith, 2002 ).

MRC response categories related thresholds explained and coded as ‘normal’ (green) or ‘abnormal’ (red)’. The first row shows the ideal graph representation for proper thresholds for the MRC grades. The first threshold at the intersection between MRC response options 0 and 1 corresponds to a 50% chance of choosing between these two adjacent categories. The thresholds should be ordered to obtain an ideal graph: Threshold 1 < Threshold 2 < Threshold 3 < Threshold 4 < Threshold 5. The second and third row give graphical examples of proper threshold ordering (coded as a green box) and disordered threshold (coded as a red box), respectively. T1–T5 = Thresholds 1–5, respectively.

Test procedure

Figure 2 presents a systematic ordering of the analyses performed in the current study. In Analyses 1 and 2 (MRC Rasch analyses for each cohort separately and MRC Rasch analyses after pooling data) the following were examined:

Step 1: the presence of ordered thresholds, thus determining whether the MRC grades for each muscle were ordered reflecting physicians’ ability to use these grades properly;

Step 2: in case of disordered thresholds: to seek for the most optimal modified MRC rescored categories that could serve as a unified tool in manual muscle scoring for all muscle groups. In order to rescore the MRC categories, the frequency distribution among the categories and the category probability curves were taken into account;

Step 3: the presence of possible item bias was examined to determine whether factors such as physician's experience in the neuromuscular field (i.e. would a more experienced physician apply the MRC grades more appropriately than a less experienced physician?) or possible differences between community and university based neurologists might influence the applicability of the MRC grades.

Study algorithm showing a systematic ordering of the analyses performed in the current study. First analyses (Analysis 1): initial MRC Rasch analysis for each individual cohort separately (thus performing a total of eight individual model analyses). Second analyses (Analysis 2): MRC Rasch analyses after pooling data at the muscle level from available cohorts. Third analyses (Analysis 3): MRC sum score Rasch analysis in patients with Guillain–Barré syndrome and CIDP. DM1 = myotonic dystrophy type-1; ICE = immune globulin intravenous for CISP; INCAT = inflammatory neuropathy cause and treatment; LGMD = limb-girdle muscular dystrophy; MMN = multifocal motor neuropathy.

Therefore, in Analyses 1 and 2, the Rasch method was applied only to examine the ability of physicians to use the MRC grading system in a proper way and to determine whether there were factors influencing its use. These analyses were not intended to create a formal Rasch-built MRC sum score for each cohort individually, since some of the cohort samples were relatively small, hence not fulfilling the basic requirements for proper Rasch modelling ( Linacre, 1994 ).

For Analysis 2, MRC data were pooled at the muscle level from the various available cohorts and resubjected to Rasch analysis, thereby controlling for diagnosis as a possible confounder and strengthening the category frequencies for the various muscles ( Linacre, 2002 ).

In Analysis 3 (MRC sum score Rasch analysis in Guillain–Barré syndrome/CIDP), the MRC 12 muscles sum score was analysed to determine whether Rasch model expectations would be met. The first two steps for Analyses 1 and 2 (see above) were also performed here. Subsequently, since there is no consensus regarding a fixed sequence of steps that must be followed when doing Rasch analyses, our rationale for the following steps were constantly driven by the biggest abnormality seen when studying all subjected data to Rasch, thereby focusing on all aspects that did not meet model expectations (misfit statistics, fit residuals disturbances, under-/overfitting, local dependency >0.3, and item bias). All steps needed were taken to create a unidimensional scale at the interval level.

Rasch general aspects, person factors and statistics

The MRC data of each muscle group were treated as if it was an ‘item’ that needed to be completed by the patients with response options from 0 to 5 (in the current study setting: a physician completed the ‘item’) using the Rasch Unidimensional Measurement Model 2020 software ( Andrich et al. , 2003 ).

In Analysis 1 (MRC Rasch in each cohort separately), the following person factors were taken into account ( Supplementary Table 2 ):

Ages: 1, <40 years; 2, 40–59 years and 3, ≥60 years;

gender: 0, female; 1, male;

type of disease: (a) inflammatory neuropathy-cause-and-treatment cohort: 1, Guillain–Barré syndrome; 2, CIDP; 3, gammopathy related polyneuropathy; (b) myotonic dystrophy cohort: 1, mild; 2, adult; 3, child/congenital type; and (c) limb-girdle dystrophy cohort: 1, sarcoglycanopathy; 2, calpainopathy; 3, limb-girdle type 1B, 2B and 2I; 4, unclassified;

duration of disease: (a) for all cohorts except limb-girdle patients: 1, <5 years; 2, 5–9 years; 3, 10–19 years; 4, ≥20 years; and (b) for limb-girdle cohort: 1, <10 years; 2, 10–19 years; 3, 20–29 years; 4, ≥30 years;

physician's experience in the neuromuscular field: for the inflammatory–neuropathy cause and treatment studies: 1, <3 years experience; 2, 3–5 years experience; 3, ≥6 years experience; the latter group constituting senior neuromuscular experts;

institution; for the Guillain–Barré syndrome trials: 0, community based; 1, university based hospital; and

country; for the Guillain–Barré syndrome cohort 2004: 1, The Netherlands; 2, Germany; 3, Belgium.

For Analyses 2 and 3 (MRC Rasch after pooling data and MRC sum score in Guillain–Barré syndrome/CIDP), the factors studied included (i) age category: 1, <40 years; 2, 40–59 years; 3, ≥60 years; (ii) gender: 0, female; 1, male; and (iii) type of disease: depending on the amount of illnesses being pooled together, each illness received a separate code.

For the MRC sum score analysis, the person separation index was also determined, which should be ≥0.7 for proper group comparison, and a minimum of 0.9 for clinical use ( Bland and Altman, 1997 ). The unrestricted partial credit Rasch model was used. Further analyses were undertaken using Stata 11.0 statistical software for Windows XP.

General aspects

A total of 1065 patients with various neuromuscular disorders were included from nine studies. Table 1 presents the patients’ characteristics. MRC data on 72 muscle groups were available ( Supplementary Table 1 , muscle groups assessed per cohort).

Analysis 1: initial MRC Rasch analyses for each cohort separately

Step 1: thresholds examination.

The obtained data (ordered thresholds coded ‘green’; disordered coded ‘red’; see Fig. 1 explaining these codes) for each muscle group in each cohort were summed, thereby creating a total of 210 muscle groups examined. A total of 165 (78.6%) muscle groups had disordered thresholds versus 45 (21.4%) with ordered thresholds. The disordered thresholds were particularly seen in the mid-response MRC category area (options 2 to 4).

Step 2: rescoring MRC categories

A panel of neuromuscular and Rasch researchers studied the category probability curves and category frequencies of the MRC data for each muscle group. Subsequently, all muscle groups were systematically rescored in order to obtain the maximum uniform amount of response options, which turned out to be four categories (instead of six). Of the 210 muscle groups rescored, 182 (86.7%) had ordered thresholds and 28 (13.3%) were still disordered. Sixteen of these disordered muscle groups were distally located (finger spreaders, flexors and extensors, grip strength, wrist extensors and flexors, foot dorsal and plantar flexors). All disordered muscle groups except two were found in the two cohorts with the lowest number of patient's records (multifocal motor neuropathy, n  = 102 and Pompe's disease, n  = 62).

Step 3: item bias examination

Eight selected person factors were used to examine possible item bias on the available muscle groups (see Supplementary Table 2 for available factors per cohort). Before rescoring, a total of 806 muscle groups (96.9%) were free of item bias, thus not being influenced by person factors like physicians’ experience. Item bias was only found in 26 muscles (3.1%; on person factor gender: 11 muscle groups had uniform differential item functioning, on disease type: eight had uniform, on disease duration: two uniform and one non-uniform, on physician's experience: two uniform, on country: one uniform, and on age: one muscle group had uniform differential item functioning). Differential item functioning findings did not change after rescoring at the individual cohort level.

Analysis 2: MRC Rasch analyses after pooling data

Similar findings were seen in the pooled data analyses. Of the 72 muscles examined, a total of 53 muscle groups (73.6%) had disordered threshold, particularly in the mid-categories ( Table 2 , ‘before rescoring’).

Results before and after rescoring the response options from six to four categories with corresponding threshold locations

A normal threshold ordering of the MRC grades is coded as ‘normal’; abnormal threshold is ‘abnormal’. See Fig. 1 , for examples, explaining these codes. Threshold location = location of the thresholds of adjacent MRC response options located on the created ruler (and expressed in logits).

Equivalent to the findings of Analysis 1 and based on the location seen of the disordered thresholds (mid-categories 2–4), all muscle groups were systematically rescored to a modified MRC with four categories. Table 2 provides the data for the rescored MRC categories (see last four columns). Ordered thresholds were restored for all muscles except the masseter muscle. A modified version of the MRC grading system was created for clinical use with the following grades: 0, paralysis; 1, severe weakness (defined as >50% loss of strength); 2, slight weakness (<50% loss of strength); and 3, normal strength. A 50% cut-off was based on the following: having four modified response options as having three thresholds (three theoretical intersections between adjacent response options: Thresholds 1, 2 and 3); half of the distance between Threshold 3 (representing the intersection between modified MRC Grades 2 and 3; location 4.3 logits) and Threshold 1 (intersection between modified grades 0 and 1; location −2.98) for all 72 muscle groups is located at 0.66 logits [−2.98 (location Threshold 1) + 0.5 × 7.28 (0.5 × distances between Threshold 3 and Threshold 1)], which is close to the mean for Threshold 2 (intersection between the modified Grades 1 and 2): 0.46.

Differential item functioning was also performed on person factors age, gender and diagnosis ( Supplementary Table 3 ). Item bias was hardly seen on age and gender. On diagnosis, 33 muscle groups (45.8%) demonstrated differential item functioning ( Supplementary Table 3 ).

Analysis 3: MRC sum score Rasch analysis in patients with Guillain–Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy

Step 0: general description of patients examined and initial findings.

A total of 619 patients from several cohorts [Guillain–Barré syndrome, n  = 480; CIDP, n  = 139; n  = 272 females (43.9%) and n  = 347 males (56.1%)] were available for these analyses ( van der Meche and Schmitz, 1992 ; The Dutch Guillain–Barré syndrome study group, 1994 ; Merkies, 2001 ; van Koningsveld et al. , 2004 ; Hughes et al. , 2008 ). The original MRC summed score failed to meet the model expectations. Misfit statistical findings for all three statistical parameters were initially seen ( Table 3 , ‘initial’ analysis).

Summary Rasch analyses statistics for the modification of MRC sum score in patients with Guillain−Barré syndrome and CIDP

In the final analysis, item and person fit residuals are acceptable, whereas chi-square is non-significant, indicating invariance across the trait. A person separation index of 0.91 indicates a reliable internal consistency. NA = not available; after performing split analyses, Rasch Unidimensional Measurement Model does not provide the opportunity to perform unidimensionality testing.

DF = degrees of freedom; PSI = person separation index.

Steps 1 and 2: thresholds examination and rescoring

Similar findings were seen here as the above-mentioned analyses. Eight muscle groups had disordered threshold. For uniformity, all 12 muscle groups were rescored to four response options, thereby restoring threshold ordering.

Step 3: local dependency and creating subsets

The following steps were driven by the strongest misfit seen to the Rasch model, which was found to be the strong local dependency findings of equivalent (right and left) muscle pairs (e.g. shoulder abductors right and left side; Spearman's correlations: ρ = 0.676–0.831). Therefore, six subsets of items were created, by combining the corresponding muscle pairs (left and right) with each other, improving the statistical parameters and resolving local dependency.

Step 4: unidimensionality examination

Based on the first principal components analysis, two comparison groups of subsets were formed with three positively loaded (arm muscle subsets) versus three negatively loaded (leg muscle subsets). The independent t -tests between these two groups suggested acceptable unidimensionality [ t -test (95% confidence interval): 0.065 (0.047–0.082)].

Step 5: item bias examination

Uniform differential item functioning was demonstrated on person factor ‘disease type’ for all created muscle subsets, except for the elbow flexors subset. Therefore, each subset of muscle pairs was split in order to obtain specific subsets for the patients with Guillain–Barré syndrome and CIDP, separately. After this, the model was free of any item bias and local dependency. All subsets of items, except the ‘foot dorsal flexors for patients with Guillain–Barré syndrome’, demonstrated fit statistics within required limits. The foot dorsal flexors in Guillain–Barré syndrome had a fit residual of +5.845 ( P  = 0.000021), which disturbed Rasch model fitting ( Table 3 , final analysis for complete model fit after removing this item). However, for practical reasons the structure of the MRC sum score (composed by 12 muscles) was maintained, despite having skewed foot dorsal flexors in the Guillain–Barré syndrome subset of item. A high person separation index (0.91) was obtained for the final modified MRC sum score model.

Manual muscle testing has been used for more than seven decades for monitoring disease progression and response to therapy in various neuromuscular disorders ( van der Meche and Schmitz, 1992 ; van der Kooi et al. , 1996 ; de Die-Smulders et al. , 1998 ; Merkies, 2001 ; Van den Berg-Vos et al. , 2002 ; van Koningsveld et al. , 2004 ; Hagemans et al. , 2005 ; Van Asseldonk et al. , 2005 ; Hughes et al. , 2008 ; Hermans et al. , 2010 ) and the MRC grading system has been widely used for this purpose ( Dyck et al. , 2005 ; Compston, 2010 ). This study systematically examined the discriminatory capacity of the MRC grading system in a broad mixture of patients with neuromuscular illnesses, assessing a large number of muscles using the Rasch method. The original six response categories of the MRC grading system failed to differentiate among patients with various degrees of muscle weakness. Three-quarters of all muscles examined demonstrated disordered thresholds, especially in the mid-response categories (options 2–4). The inability of physicians to apply the apparently intuitive and easily applicable MRC grades in a proper way is consistent with reports criticizing the MRC system ( Dyck et al. , 2005 ; Schreuders et al. , 2006 ; MacAvoy and Green, 2007 ; Merlini, 2010 ). The current paper also shows that the observed disordered thresholds were generally independent of factors such as physicians’ experience, duration of illness or type of practice (university- versus community-based). The original MRC grading system inconsistencies were also ‘cross-validated’ throughout the neuromuscular cohorts, as the findings between the individual disease cohorts were equivalent.

After systematically rescoring all MRC grades to a modified four category response option, the accuracy of the MRC grading system increased by fulfilling ordered thresholds requirements. While this change from six to four response options might intuitively lower the ability to capture functional changes in a patient, from the current evidence, however, keeping the six responses will give a false sense of precision and potentially increase the error in assessment, which may lead to a false sense of clinically meaningful improvement when it may not exist.

The current paper shows the difficulties with the use of summed scores derived from various muscles tested in patients with Guillain–Barré syndrome and CIDP. However, after Rasch modelling, we were able to present a transformed modified MRC 12 muscle groups summed score for use in future clinical studies in these disorders ( Kleyweg et al. , 1991 ). The analyses revealed severe misfit of the foot dorsal flexors. However, since Guillain–Barré syndrome and CIDP are length-dependent neuropathies, we decided to keep this muscle group in the final model. The presented Rasch-built modified interval MRC sum score is considered a substantial improvement compared to the evaluation of muscle strength using ordinal based scores, which in essence are not suitable for performing adequate statistics. The modified interval MRC sum score for patients with CIDP should, however, be applied with some caution, because only 139 patients were assessed, which is lower than the proposed sample size requirements for a stable model ( Linacre, 1994 ). Also, the responsiveness of the Rasch-built modified interval MRC summed score for patients with Guillain–Barré syndrome and CIDP needs to be demonstrated in longitudinal studies, which is currently being investigated ( Liang, 1995 ). However, its personal separation index was high, indicating good ability of the modified scale to differentiate between groups of patients with various degrees for muscle weakness. Finally, since the differential item functioning findings on diagnosis ( Supplementary Table 3 ) demonstrate that neuromuscular illnesses may behave differently, it is conceivable that Rasch-built MRC sum scores are needed for specific illnesses such as multifocal motor neuropathy and other neuromuscular diseases. These efforts should be the focus of future studies.

In conclusion, the original MRC manual muscle testing grading system failed to meet the Rasch model expectations in various neuromuscular disorders, despite being the standard metric in neurology worldwide. Modification of this grading system to four response categories (0, paralysis; 1, severe weakness; 2, slight weakness; and 3, normal strength) may significantly enhance the ability of clinicians to differentiate degrees of weakness with greater precision and accuracy. Based on this, we have developed a Rasch-built interval MRC summed score for use in future clinical studies evaluating patients with Guillain–Barré syndrome and CIDP. Future studies are warranted to improve the solidness of our neurological assessments.

Supplementary Material

Supplementary material is available at Brain online.

Acknowledgements

We thank Professor S. Waxman from the Yale University, USA who helped us to increase the transparency and reading of the manuscript. The members of PeriNomS Study Group are as follows: A.A. Barreira, Brazil; D. Bennett, UK; P.Y.K. van den Bergh, Belgium; V. Bril, Canada; G. Devigili, Italy; R.D. Hadden, UK; A.F. Hahn, Canada; H.-P. Hartung, Germany; R.A.C. Hughes, UK; I. Illa, Spain; H. Katzberg, Canada; A.J. van der Kooi, The Netherlands; J.-M. Léger, France; R.A. Lewis, USA; M.P.T. Lunn, UK; O.J.M. Nascimento, Brazil; E. Nobile-Orazio, Italy; L. Padua, Italy; J. Pouget, France; M.M. Reilly, UK, I. van Schaik, The Netherlands; B. Smith, USA; M. de Visser, The Netherlands; D. Walk, USA

Abbreviations

chronic inflammatory demyelinating polyradiculoneuropathy

Medical Research Council

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