Impairments of the ipsilesional upper-extremity in the first 6-months post-stroke
Bibliographic record
Abstract
BACKGROUND: Ipsilesional motor impairments of the arm are common after stroke. Previous studies have suggested that severity of contralesional arm impairment and/or hemisphere of lesion may predict the severity of ipsilesional arm impairments. Historically, these impairments have been assessed using clinical scales, which are less sensitive than robot-based measures of sensorimotor performance. Therefore, the objective of this study was to characterize progression of ipsilesional arm motor impairments using a robot-based assessment of motor function over the first 6-months post-stroke and quantify their relationship to (1) contralesional arm impairment severity and (2) stroke-lesioned hemisphere. METHODS: A total of 106 participants with first-time, unilateral stroke completed a unilateral assessment of arm motor impairment (visually guided reaching task) using the Kinarm Exoskeleton. Participants completed the assessment along with a battery of clinical measures with both ipsilesional and contralesional arms at 1-, 6-, 12-, and 26-weeks post-stroke. RESULTS: Robotic assessment of arm motor function revealed a higher incidence of ipsilesional arm impairment than clinical measures immediately post-stroke. The incidence of ipsilesional arm impairments decreased from 47 to 14% across the study period. Kolmogorov-Smirnov tests revealed that ipsilesional arm impairment severity, as measured by our task, was not related to which hemisphere was lesioned. The severity of ipsilesional arm impairments was variable but displayed moderate significant relationships to contralesional arm impairment severity with some robot-based parameters. CONCLUSIONS: Ipsilesional arm impairments were variable. They displayed relationships of varying strength with contralesional impairments and were not well predicted by lesioned hemisphere. With standard clinical care, 86% of ipsilesional impairments recovered by 6-months post-stroke.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".