Efficacy of using robotics to characterize impaired upper-limb function in a non-human primate model of stroke
Bibliographic record
Abstract
Rationale: Stroke is a leading cause of death and disability and has the largest socioeconomic burden of any disease in Canada. Unfortunately, thousands of stroke therapies that proved successful in animal preclinical trials failed in subsequent human populations. Non-human primates (NHPs) closely resemble humans and may represent an animal model that could help bridge the translational gap preceding clinical trials. Additionally, robotic technology has been proven to provide objective determination of outcomes in human stroke populations and it is possible that these outcomes are conserved across species. This study investigated the efficacy of using robotic tasks as a behavioural assessment tool in a NHP model of stroke. Methods: Stroke was induced in 2 cynomolgus macaques through transient 90-minute right middle cerebral artery occlusion. At 2.5 years post-stroke, neurobehavioural outcomes were assessed using a visually guided reaching (VGR) and a postural perturbation (PP) KINARM exoskeleton robotic task. Stroke NHP task parameters were compared to control performance (2 healthy, age matched controls) for both the affected and unaffected-arms to determine impairment. Results: In the VGR task, stroke animals made reaches with their affected-arm that were less accurate, had more corrective motions, travelled a greater distance, and took longer as compared to controls (p<0.01). In the PP task, responses of stroke animals to perturbations to the affected-arm were further displaced, took longer to stop, had more corrective motions, and took longer to return to centre as compared to controls (p<0.01). Several, specific unaffected-arm deficits were also identified for both stroke NHPs. Conclusions: The KINARM tasks were able to consistently quantify specific sensorimotor deficits in stroke NHPs in a way similar to that previously achieved in human populations. This study proves the efficacy of robotic assessment in a NHP model of stroke and supports the feasibility of this model in translating future stroke therapies from preclinical to clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".