Assessment of Sex Differences in Recovery of Motor and Sensory Impairments Poststroke
Bibliographic record
Abstract
Background. Understanding potential sex differences in stroke recovery is important for prognosis, ensuring appropriate allocation of health care resources, and for stratification in research studies. Previously, functional measures have shown poorer outcomes for females, however, little is known about sex differences that may exist in specific motor and sensory impairments. Objective. The aim of this study was to utilize robotic assessments of motor and sensory impairments to determine if there are sex differences at the impairment level in stroke recovery over the first 6 months poststroke. Methods. We used robotic and clinical assessments of motor and sensory impairments at 1, 6, 12, and 26 weeks poststroke in 108 males and 52 females. Linear mixed models were used to examine the effect of sex on recovery poststroke, controlling for age and lesion volume. Results. In general, we did not find significant sex differences across a range of assessments. The exception to this was a sex × age interaction for the Purdue Pegboard Assessment, where we found that females had better performance than males at younger ages (<62 years), but males had better performance at older ages. Conclusions. While recruitment biases need to be acknowledged when generalizing our results to stroke recovery at-large, our results suggest that sex differences do not exist at the impairment level poststroke.
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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 | 0.002 | 0.004 |
| 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) | 0.003 | 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".