Sex differences in delayed cerebral ischemia after subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE In this study the authors sought to investigate the sex differences in the risk of delayed cerebral ischemia (DCI), delayed cerebral infarction, and the role of hormonal status. METHODS Ten studies included in the SAHIT (SAH International Trialists) repository were analyzed using a fitting logistic regression model. Heterogeneity between the studies was tested using I2 statistics, and the results were pooled using a random-effects model. Multivariable analysis was adjusted for the effects of neurological status and fixed effect of study. An additional model was examined in which women and men were split into groups according to an age cut point of 55 years, as a surrogate to define hormonal status. RESULTS A pooled cohort of 6713 patients was analyzed. The risk of DCI was statistically significantly higher in women than in men (OR 1.29, 95% CI 1.12–1.48); no difference was found with respect to cerebral infarction (OR 1.17, 95% CI 0.98–1.40). No difference was found in the risk of DCI when comparing women ≤ 55 and > 55 years (OR 0.87, 95% CI 0.74–1.02; p = 0.08) or when comparing men ≤ 55 and > 55 years (p = 0.38). Independent predictors of DCI were World Federation of Neurosurgical Societies (WFNS) grade, Fisher grade, age, and sex. Independent predictors of infarction included WFNS grade, Fisher grade, and aneurysm size. CONCLUSIONS Female sex is associated with a higher risk of DCI. Sex differences may play a role in the pathogenesis of DCI but are not associated with menopausal status. The predictors of DCI and cerebral infarction were identified in a very large cohort and reflect experience from multiple institutions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".