Thirty-day mortality in females after elective and urgent abdominal aortic aneurysm repair
Bibliographic record
Abstract
INTRODUCTION: Female sex is a risk factor of post-operative mortality and morbidity after abdominal aortic aneurysm (AAA) repair. The aim of this systematic review is to assess the sex-specific early mortality following both elective and urgent AAA repair.EVIDENCE ACQUISITION: The Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed. Observational studies (2000-2022), of the English medical literature, focusing on early mortality after AAA repair in females under elective or urgent setting were eligible. A systematic search of MEDLINE, EMBASE and CENTRAL databases, was conducted (November 30th, 2022). The risk of bias was assessed using the Newcastle-Ottawa Scale. Primary outcome was 30-day mortality in relevant strata. A proportional metanalysis was used to assess the estimates.EVIDENCE SYNTHESIS: Seventeen retrospective studies and 83,738 females were included. Thereof 68.7% underwent elective repair while the remaining were managed urgently. Endovascular repair (EVAR) was applied in 37.3% of patients (15.4% urgent) vs. 62.7% with OSR (23.5% urgent). In the total cohort, the perioperative mortality was estimated at 11% (OR, 95% CI: 5-17%, P<0.01, I2 99.92%) while 3% (OR, 95% CI: 0.02-0.03, P<0.01, I2 93.42%) deceased after elective repair (2% OR, 95% CI 0.01-0.02, P<0.01, I2 83.08%, after EVAR and 5% (OR, 95% CI: 0.05-0.06, P<0.01, I2 77.36%, after OSR) and 36% (OR, 95% CI: 0.28-0.44, P<0.01, I2 99.51%) after urgent repair (25% OR, 95% CI: 0.16-0.34, P<0.01, I2 98.45%, after EVAR and 40% (OR, 95% CI: 0.34-0.46, P<0.01, I2 95.96%, after OSR).CONCLUSIONS: AAA repair in females appears to be associated with considerable postoperative mortality. Despite the rapid development of innovative techniques and intensive care of severely ill patients, perioperative mortality after ruptured AAA remains devastatingly high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".