Gauging Recovery after Hemorrhagic Stroke in Rats: Implications for Cytoprotection Studies
Bibliographic record
Abstract
Successful clinical translation of prospective cytoprotectants will likely occur only with treatments that improve functional recovery in preclinical (rodent) studies. Despite this assumption, many rely solely on histopathologic end points or the use of one or two simple behavioral tests. Presently, we used a battery of tests to gauge recovery after a unilateral intracerebral hemorrhagic stroke (ICH) targeting the striatum. In total, 60 rats (N=15 per group) were stereotaxically infused with 0 (SHAM), 0.06 (MILD lesion), 0.12 (MODERATE lesion), or 0.18 U (SEVERE lesion) of bacterial collagenase. This created a range of injury akin to moderate (from SEVERE to MODERATE or MODERATE to MILD lesion size approximately 30% reduction) and substantial cytoprotection (SEVERE to MILD lesion size--51% reduction). Post-ICH functional testing occurred over 30 days. Tests included the horizontal ladder and elevated beam tests, swimming, limb-use asymmetry (cylinder) test, a Neurologic Deficit Scale, an adhesive tape removal test of sensory neglect, and the staircase and single pellet tests of skilled reaching. Most tests detected significant impairments (versus SHAM), but only a few (e.g., staircase) frequently distinguished among ICH groups and none consistently differentiated among all ICH groups. However, by using a battery of tests we could behaviorally distinguish groups. Thus, preclinical testing would benefit from using a battery of behavioral tests as anything less may miss treatment effects. Such testing must be based on factors including the type of lesion, the postoperative delay and the time required to complete testing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".