Does Stroke Rehabilitation Really Matter? Part A: Proportional Stroke Recovery in the Rat
Bibliographic record
Abstract
BACKGROUND: In human upper-limb stroke, initial level of functional impairment or corticospinal tract injury can accurately predict the degree of poststroke recovery, independent of rehabilitation practices. This proportional recovery rule implies that current rehabilitation practices may play little or no role in brain repair, with recovery largely a result of spontaneous biological recovery processes. OBJECTIVE: The present study sought to determine if similar biomarkers predict recovery of poststroke function in rats, indicating that an endogenous biological recovery process might be preserved across mammalian species. METHODS: Using a cohort of 593 male Sprague-Dawley rats, we predicted poststroke change in pellet retrieval in the Montoya staircase-reaching task based on initial impairment alone. Stratification of the sample into "fitters" and "nonfitters" of the proportional recovery rule using hierarchical cluster analysis allowed identification of distinguishing characteristics of these subgroups. RESULTS: Approximately 30% of subjects were identified as fitters of the rule. These rats showed recovery in proportion to their initial level of impairment of 66% (95% CI = 62%-70%). This interval overlaps with those of multiple human clinical trials. A number of variables, including less severe infarct volumes and initial poststroke impairments distinguished fitters of the rule from nonfitters. CONCLUSIONS: These findings suggest that proportional recovery is a cross-species phenomenon that can be used to uncover biological mechanisms contributing to stroke recovery.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".