Assessments of Upper Limb Ability following Stroke: A Review
Bibliographic record
Abstract
The aim of this paper was to review the psychometric properties and clinical utility of assessments of upper limb ability following stroke. Upper limb ability was defined as the use of the arm in meaningful activity. The database searches identified 13 assessments of upper limb ability. The inclusion criteria were (a) an evaluation of upper limb ability post-stroke, (b) a quantitative assessment yielding a numerical score, (c) able to be administered by an occupational therapist and (d) published information building on the results of a review in 2001. The assessments included were reviewed with respect to the ability measured, equipment required, evidence of reliability and validity, and advantages and disadvantages of use. Seven assessments satisfied the inclusion criteria. These assessments have undergone varying degrees of psychometric testing as outcome measures for upper limb recovery and all demonstrate at least adequate levels of reliability and validity. There is variation in the availability of instruction manuals and time commitments for test administration. All test items are generally available and inexpensive, enabling departments to set up the assessments with minimal financial outlay. The assessments described are regularly used in research and all demonstrate psychometric properties that suggest that they could be incorporated into clinical practice. Occupational therapists are encouraged to consider how they may include these assessments into their own clinical practice. An important step in this process may be further research into the clinical utility of these assessments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".