Effect of pharmaceutical treatment on vasospasm, delayed cerebral ischemia, and clinical outcome in patients with aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis
Bibliographic record
Abstract
As it is often assumed that delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) is caused by vasospasm, clinical trials often focus on prevention of vasospasm with the aim to improve clinical outcome. However, the role of vasospasm in the pathogenesis of DCI and clinical outcome is possibly smaller than previously assumed. We performed a systematic review and meta-analysis on all randomized, double-blind, placebo-controlled trials that studied the effect of pharmaceutical preventive strategies on vasospasm, DCI, and clinical outcome in SAH patients to further investigate the relationship between vasospasm and clinical outcome. Effect sizes were expressed in pooled risk ratio (RR) estimates with corresponding 95% confidence intervals (CI). A total of 14 studies randomizing 4,235 patients were included. Despite a reduction of vasospasm (RR 0.80 (95% CI 0.70 to 0.92)), no statistically significant effect on poor outcome was observed (RR 0.93 (95% CI 0.85 to 1.03)). The variety of DCI definitions did not justify pooling the DCI data. We conclude that pharmaceutical treatments have significantly decreased the incidence of vasospasm, but not of poor clinical outcome. This dissociation between vasospasm and clinical outcome could result from methodological problems, sample size, insensitivity of clinical outcome measures, or from mechanisms other than vasospasm that also contribute to poor outcome.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".