Characterization of muscle strength using the strength domain of the stroke impact scale: An integrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Muscle weakness is among the most common and obvious impairments in older adults and individuals with neurologic disorders. Although impairments in muscle strength are typically characterized using performance measures, the impairments have also been described using patient or observer-report. The objective of this review was to summarize literature describing use of a patient-report instrument, the Strength Domain (SD) of the Stroke Impact Scale (SIS), to grade strength impairments. METHODS: Peer-reviewed literature reporting SD scores for the SIS was identified using computerized searches of the CINAHL, PubMed, and Scopus databases followed by hand searches. Potentially relevant articles were then mined for data on the participants tested, the SIS version used, scores documented, and clinimetric properties reported. RESULTS: Sixty-five articles were judged appropriate based on inclusion and exclusion criteria. The articles involved more than 7000 residents of 22 countries. All articles focused on individuals with stroke (usually chronic), although one also included community-dwelling adults without stroke. The SIS version used was frequently unreported, but 3.0 was version most often specified. For articles reporting SD scale scores the mean ranged from 19.7 to 85.5. Construct (known groups, convergent, and discriminant) validity of the SD was supported by the literature as was its internal consistency and test-retest reliability. Responsiveness of the SD was evinced by numerous studies showing increases in SD scores with time or accompanying effective interventions. However, only one study indicated responsiveness using an anchor-based statistic. CONCLUSIONS: The SD of the SIS is a well-established and mostly clinimetrically sound patient-report measure of paretic limb strength among individuals with stroke. Its use with individuals with weakness accompanying aging or diagnoses other than stroke remains to be substantiated.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".