Value of risk scores in the decision to palliate patients with ruptured abdominal aortic aneurysm
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to develop a 48-h mortality risk score, which included morphology data, for patients with ruptured abdominal aortic aneurysm presenting to an emergency department, and to assess its predictive accuracy and clinical effectiveness in triaging patients to immediate aneurysm repair, transfer or palliative care. METHODS: Data from patients in the IMPROVE (Immediate Management of the Patient With Ruptured Aneurysm: Open Versus Endovascular Repair) randomized trial were used to develop the risk score. Variables considered included age, sex, haemodynamic markers and aortic morphology. Backwards selection was used to identify relevant predictors. Predictive performance was assessed using calibration plots and the C-statistic. Validation of the newly developed and other previously published scores was conducted in four external populations. The net benefit of treating patients based on a risk threshold compared with treating none was quantified. RESULTS: Data from 536 patients in the IMPROVE trial were included. The final variables retained were age, sex, haemoglobin level, serum creatinine level, systolic BP, aortic neck length and angle, and acute myocardial ischaemia. The discrimination of the score for 48-h mortality in the IMPROVE data was reasonable (C-statistic 0·710, 95 per cent c.i. 0·659 to 0·760), but varied in external populations (from 0·652 to 0·761). The new score outperformed other published risk scores in some, but not all, populations. An 8 (95 per cent c.i. 5 to 11) per cent improvement in the C-statistic was estimated compared with using age alone. CONCLUSION: The assessed risk scores did not have sufficient accuracy to enable potentially life-saving decisions to be made regarding intervention. Focus should therefore shift to offering repair to more patients and reducing non-intervention rates, while respecting the wishes of the patient and family.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".