Paretic Upper-Limb Strength Best Explains Arm Activity in People With Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to determine the relationship among variables of upper-limb impairment, upper-limb performance in activities of daily living (activity), and engagement in life events and roles (participation) in people with chronic stroke. SUBJECTS: The subjects were 93 community-dwelling individuals with stroke (> or =1 year). METHODS: This study, which was conducted in a tertiary rehabilitation center, used a cross-sectional design. The main measures of impairment were the Modified Ashworth Scale, handheld dynamometry, sensory testing (monofilaments), and the Brief Pain Inventory. The main measures of activity were the Chedoke Arm and Hand Activity Inventory (CAHAI) and the Motor Activity Log (MAL). The main measure of participation was the Reintegration to Normal Living (RNL) Index. RESULTS: Paretic upper-limb strength (force-generating capacity) (r=.89, P<.01), grip strength (r=.69, P<.01), and tone (resistance to passive movement) (r=-.80, P<.01) were the impairment variables that were most strongly related to activity. Tone (r=-.23, P<.05) and CAHAI scores (r=.22, P<.05) had a significant, but weak, relationship to participation. Upper-limb strength accounted for 87% of the variance of the CAHAI scores and 78% of the variance of the MAL scores. In the participation models, tone and CAHAI scores accounted for 5% of the variance of the RNL Index scores. DISCUSSION AND CONCLUSION: Paretic upper-limb strength had the strongest relationship with variables of activity and best explained upper-limb performance in activities of daily living. Grip strength, tone, and sensation also were factors of upper-limb performance in activities of daily living. Increased tone and upper-limb performance in activities of daily living had a weak relationship with participation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".