Meta-analysis of timing of endovascular aneurysm treatment in subarachnoid haemorrhage: inconsistent results of early treatment within 1 day
Bibliographic record
Abstract
Background and purpose To systematically review and meta-analyse the data on impact of timing of endovascular treatment in aneurysmal subarachnoid haemorrhage (SAH) to determine if earlier treatment is associated with improved clinical outcomes and reduced case fatality. Methods We searched MEDLINE, Cochrane database, EMBASE and Web of Science to identify studies for inclusion. The measures of effect utilised were unadjusted/adjusted ORs. Effect estimates were combined using random effects models for each outcome (poor outcome, case fatality); heterogeneity was assessed using the I2 index. Subgroup and sensitivity analyses were performed to account for heterogeneity and risk of bias. Results 16 studies met the inclusion criteria. Treatment <1 day was associated with a reduced odds of poor outcome compared with treatment >1 day (OR=0.40 (95% CI 0.28 to 0.56; I2=0%)) but not when compared with treatment at 1–3 days (OR=1.16 (95% CI 0.47 to 2.90; I2=81%)). Treatment at <2 days and at <3 days were associated with similar odds of poor outcome compared with later treatment (OR=1.20 (95% CI 0.70 to 2.05; I2=73%; OR=0.71 (95% CI 0.36 to 1.37; I2=71%)). Early treatment was associated with similar odds of case fatality compared with later treatment, regardless of how early/late treatment were defined (OR=1.80 (95% CI 0.88 to 3.67; I2=34%) for treatment <1 day vs days 1–3; OR=1.71 (95% CI 0.72 to 4.03; I2=54%) for treatment <2 days vs later; OR=0.90 (95% CI 0.31 to 2.68; I2=48%) for treatment <3 days vs later). Conclusions In only 1 of the analyses was there a statistically significant result, which favoured treatment <1 day. The inconsistent results and heterogeneity within most analyses highlight the lack of evidence for best timing of endovascular treatment in SAH patients.
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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 | 0.036 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.063 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".