ABC score: a new risk score that accurately predicts mortality in acute upper and lower gastrointestinal bleeding: an international multicentre study
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".