Long-term functional recovery and compensation after cerebral ischemia in rats
Bibliographic record
Abstract
Cerebral ischemia is one of the most common causes of disabilities in adults and leads to long-term motor and cognitive impairments with limited therapeutic possibilities. Treatment options have proven efficient in preclinical models of cerebral ischemia but have failed in the clinical setting. This limited translation may be due to the suitability of models used and outcomes measured as most studies have focused on the early period after injury with gross motor scales, which have limited correlation to the clinical situation. The aim of this study was to determine long-term functional outcomes after cerebral ischemia in rats, focusing on fine motor function, social and depressive behavior as clinically relevant measures. A secondary objective was to evaluate the effects of an anti-inflammatory treatment (interleukin-1 receptor antagonist (IL-1Ra)) on functional recovery and compensation. Infarct volume was correlated with long-term (25 days) impairments in fine motor skills, but not with emotional components of behavior. Motor impairments could not be detected using conventional neurological tests and only detailed analysis allowed differentiation between recovery and compensation. Acute systemic administration of IL-1Ra (at reperfusion) led to a faster and more complete recovery, but delayed (24h) IL-1Ra treatment had no effect. In summary functional assessment after brain injury requires detailed motor tests in order to address long-term impairments and compensation processes that are mediated by intact tissues. Functional deficits in skilled movement after brain injury represent ideal predictors of long-term outcomes and should become standard measures in the assessment of preclinical animal models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".