Individuals with the Dominant Hand Affected following Stroke Demonstrate Less Impairment Than Those with the Nondominant Hand Affected
Bibliographic record
Abstract
OBJECTIVE: The purpose was to determine if upper extremity impairment and function in individuals with chronic stroke is dependent upon whether the dominant or non-dominant hand is affected. METHODS: Ninety-three community-dwelling individuals with stroke. The Modified Ashworth Scale (tone), handheld dynamometry (isometric strength), monofilaments (sensation), Brief Pain Inventory (pain), Chedoke Arm and Hand Activity Inventory Motor Activity Log (paretic arm use), and Reintegration to Normal Living Index (participation) were used to form impairment and function models. RESULTS: Multivariate analysis models (Dominance x Severity) were created for impairment and function variables. There was a significant interaction and main effect of Dominance for the impairment model (P = 0.01) but not the function model (P = 0.75). The dependent variables of tone, grip strength, and pain were all significantly affected by Dominance, indicating less impairment if the dominant hand was affected. All dependent variables except pain were affected by Severity. CONCLUSION: This study looked at the effect of the dominant hand being affected versus the nondominant in individuals with chronic stroke. Individuals with the dominant hand affected demonstrated less impairment than those with the nondominant hand affected. However, there was no effect of dominance on paretic arm use or performance in activities of daily living. Prospective studies to further explore the issue of hand dominance and poststroke function are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".