Review of Scoring Systems for Predicting 30-Day Mortality in Ruptured Abdominal Aortic Aneurysm
Bibliographic record
Abstract
BACKGROUND: Ruptured abdominal aortic aneurysms (rAAAs) are a serious disease that can lead to high mortality; thus, their early prediction can save patients' lives. The aim of this study was to compare the accuracies of various models for predicting rAAA mortality-including the Glasgow Aneurysm Score, Vancouver Scoring System, Dutch Aneurysm Score, Edinburgh Ruptured Aneurysm Score (ERAS), and Hardman index-based on rAAA treatment outcomes at our institution. METHODS: Between 2016 and 2022, we retrospectively analyzed the early outcome data-including 30-day mortality-of patients who underwent emergency surgery for rAAA at our institution. Receiver operating characteristic curve analysis was performed to compare the aneurysm scoring systems for mortality using the area under the receiver operating characteristic curve (AUC). RESULTS: The AUC was better for the ERAS (0.718; 95% confidence interval, 0.601-0.817) than for the other scoring systems. Significant differences were observed between ERAS and Hardman indices (difference: 0.179; P = 0.016). No significant differences were found among the Glasgow Aneurysm Score, Vancouver Scoring System, and Dutch Aneurysm Score predictive risk models. CONCLUSIONS: Among the models for predicting mortality in patients with rAAA, the ERAS model demonstrated the highest AUC value; however, significant differences were only observed between ERAS and Hardman indices. This study may help develop strategies for improving rAAA prediction.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".