Evaluation of dexamethasone for the treatment of intracerebral hemorrhage using a collagenase‐induced intracerebral hematoma model in rats
Bibliographic record
Abstract
Dexamethasone was evaluated for the treatment of intracerebral hemorrhage using a rat model of cerebral hematoma induced by intracerebral injection of collagenase. The treatment group consisted of hematoma rats receiving dexamethasone 1 mg/kg intraperitoneal (i.p.) at 1 and 24 h following surgery. Controls included hematoma rats receiving saline i.p. and sham-operated animals receiving saline i.p. Each animal was evaluated neurologically prior to, as well as 24 and 48 h following surgery. After the last neurological evaluation, animals were deeply anesthetized and the brain was removed following perfusion for microscopic examination and glial fibrillary acidic protein immunohistochemistry. Behavioral scores were significantly improved in the treated group (P < 0.0001). The hematoma volume was significantly smaller (P < 0.02). Neutrophils and astrocytes were less numerous in the hematoma of dexamethasone-treated animals (P < 0.001), however the number of necrotic neurons in the penumbra was not changed by the treatment. The number of necrotic neurons in the cerebral cortex was less in treated than in nontreated animals (P < 0.01). Controls had many vascular changes including necrotic endothelium and fibrin deposits compared with treated animals. In conclusion, dexamethasone administered shortly after an intracerebral hematoma appears beneficial for the treatment of this condition.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".