Pharmacologic Reduction of Angiographic Vasospasm in Experimental Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Animal models have been developed to simulate angiographic vasospasm secondary to subarachnoid hemorrhage (SAH) and to test pharmacologic treatments. Our aim was to evaluate the effect of pharmacologic treatments that have been tested in humans and in preclinical studies to determine if animal models inform results reported in humans. A systematic review and meta-analysis of SAH studies was performed. We investigated predictors of translation from animals to humans with multivariate logistic regression. Pharmacologic reduction of vasospasm was effective in mice, rats, rabbits, dogs, nonhuman primates (standard mean difference of -1.74; 95% confidence interval -2.04 to -1.44) and humans. Animal studies were generally of poor methodologic quality and there was evidence of publication bias. Subgroup analysis by drug and species showed that statins, tissue plasminogen activator, erythropoietin, endothelin receptor antagonists, calcium channel antagonists, fasudil, and tirilazad were effective whereas magnesium was not. Only evaluation of vasospasm >3 days after SAH was independently associated with successful translation. We conclude that reduction of vasospasm is effective in animals and humans and that evaluation of vasospasm >3 days after SAH may be preferable for preclinical 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".