Chronic Poststroke Deficits in Gross and Fine Motor Control of the Ipsilesional Upper Limb
Bibliographic record
Abstract
OBJECTIVES: Individuals with stroke often experience contralesional and ipsilesional arm motor deficits. The aim of this study was to compare fine and gross motor hand dexterity of the ipsilesional hand post-stroke with controls, normative values, and the contralesional hand. DESIGN: Data were collected from right-handed individuals with chronic stroke (n = 20), age-/sex-matched controls (n = 10), and normative values (n = 20) performing the Nine-Hole Peg Test and the Box and Blocks Test. RESULTS: Individuals with stroke demonstrated poorer performance with the ipsilesional arm relative to both the control group (mean difference [95% confidence interval]: Nine-Hole Peg Test [seconds], 3.4 [-0.5 to 7.3]; Box and Blocks Test [number of blocks], -12.3 [-20.3 to -4.2]) and normative values (mean difference [95% confidence interval]: Nine-Hole Peg Test [seconds], 6.5 [4.0-9.1]; Box and Blocks Test (number of blocks), -15.3 [-20.1 to -10.5]). Ipsilesional arm performance was significantly better than performance with the contralesional arm (mean difference [95% confidence interval]: Nine-Hole Peg Test [seconds], -9.4 [-20.2 to 1.4]; Box and Blocks Test (number of blocks), 33.2 [20.9-45.5]). CONCLUSION: These findings identify residual deficits in fine and gross dexterity of the ipsilesional hand in commonly used outcome measures of hand manipulation among individuals with chronic stroke. Possible underlying mechanisms and clinical relevance are discussed.
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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 | 0.001 |
| 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) | 0.002 | 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".