ABC score: a new risk score that accurately predicts mortality in acute upper and lower gastrointestinal bleeding: an international multicentre study
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Bibliographic record
Abstract
OBJECTIVES: Existing scores are not accurate at predicting mortality in upper (UGIB) and lower (LGIB) gastrointestinal bleeding. We aimed to develop and validate a new pre-endoscopy score for predicting mortality in both UGIB and LGIB. DESIGN AND SETTING: International cohort study. Patients presenting to hospital with UGIB at six international centres were used to develop a risk score for predicting mortality using regression analyses. The score's performance in UGIB and LGIB was externally validated and compared with existing scores using four international datasets. We calculated areas under receiver operating characteristics curves (AUROCs), sensitivities, specificities and outcome among patients classified as low risk and high risk. PARTICIPANTS AND RESULTS: We included 3012 UGIB patients in the development cohort, and 4019 UGIB and 2336 LGIB patients in the validation cohorts. Age, Blood tests and Comorbidities (ABC) score was closer associated with mortality in UGIB and LGIB (AUROCs: 0.81-84) than existing scores (AUROCs: 0.65-0.75; p≤0.02). In UGIB, patients with low ABC score (≤3), medium ABC score (4-7) and high ABC score (≥8) had 30-day mortality rates of 1.0%, 7.0% and 25%, respectively. Patients classified low risk using ABC score had lower mortality than those classified low risk with AIMS65 (threshold ≤1) (1.0 vs 4.5%; p<0.001). In LGIB, patients with low, medium and high ABC scores had in-hospital mortality rates of 0.6%, 6.3% and 18%, respectively. CONCLUSIONS: In contrast to previous scores, ABC score has good performance for predicting mortality in both UGIB and LGIB, allowing early identification and targeted management of patients at high or low risk of death.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it