Assessment of Upper Extremity Function in Multiple Sclerosis: Review and Opinion
Bibliographic record
Abstract
Upper extremity (UE) dysfunction may be present in up to ~80% of individuals with multiple sclerosis (MS), although its importance may be under-recognized relative to walking impairment, which is the hallmark symptom of MS. Upper extremity dysfunction affects independence and can impact the ability to use walking aids. Under-recognition of UE dysfunction may result in part from limited availability of performance-based and patient self-report measures that are validated for use in MS and that can be readily incorporated into clinical practice for screening and regularly scheduled assessments. In addition to the 9-Hole Peg Test, which is part of the Multiple Sclerosis Functional Composite, there are several performance-based measures that are generally used in the rehabilitation setting. These measures include the Box and Block Test, the Action Research Arm Test, the Test d'Evaluation de la performance des Membres Supérieurs des Personnes Agées, and the Jebsen-Taylor Test of Hand Function. Several of these measures were developed for use in stroke, although in contrast to stroke, which is characterized by unilateral dysfunction, UE impairment in MS is generally bilateral, and should be assessed as such. Similarly, patient-reported UE measures are available, including Disabilities of the Arm, Shoulder, and Hand (DASH) and its shorter version, QuickDASH, the Manual Ability Measure, and ABILHAND, although none has been psychometrically validated for MS. Recently, item response theory was used to develop a Neuro-QOL (Quality of Life) UE measure and a Patient-Reported Outcomes Measurement Information System UE measure; neither of these have demonstrated sensitivity to change, limiting their use for longitudinal assessment. Consequently, although work is still needed to develop and validate performance-based and patient-reported measures of UE function that are suitable for use in daily MS clinical practice, currently available UE measures can be recommended for incorporation into MS management, albeit with an understanding of their limitations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".