MECHANISMS OF MOTOR RECOVERY IN A NON-HUMAN PRIMATE MODEL OF CHRONIC STROKE
Bibliographic record
Abstract
Stroke is the leading cause of death and disability and has the largest socioeconomic burden of any disease in Canada. Survivors are frequently left with long-term disabilities that diminish their autonomy and quality of life and result in the need for chronic care. As such, there is an urgent need for therapies that improve stroke recovery, as well as accurate and quantitative tools to measure function. Non-human primates closely resemble humans in neuroanatomy and upper limb function, and may be crucial in bridging the translational gap for testing stroke therapies. Cynomolgus macaques were trained on a visually guided reaching task on the Kinesiological Instrument for Normal and Altered Reaching Movements (KINARM). We then induced strokes in these animals by transiently occluding the medial cerebral artery for 90 minutes and assessed their motor performance on the same reaching task throughout recovery. During the weeks following stroke, we noted recovery of function that plateaued by two months post-stroke. In comparing performance in this chronic stroke state to performance pre-stroke, we found that hand movements in the task became slower, less accurate, and less stereotyped. Taken together, these studies highlight specific sensorimotor deficits in visually guided reaching movements following stroke and validate the use of robotic assessment tools in identifying and quantifying such deficits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".