A multicentre development and validation study of a novel lower gastrointestinal bleeding score—The Birmingham Score
Bibliographic record
Abstract
PURPOSE: Lower gastrointestinal bleeding (LGIB) is common and risk stratification scores can guide clinical decision-making. There is no robust risk stratification tool specific for LGIB, with existing tools not routinely adopted. We aimed to develop and validate a risk stratification tool for LGIB. METHODS: Retrospective review of LGIB admissions to three centres between 2010 and 2018 formed the derivation cohort. Using regressional analysis within a machine learning technique, risk factors for adverse outcomes were identified, forming a simple risk stratification score-The Birmingham Score. Retrospective review of an additional centre, not included in the derivation cohort, was performed to validate the score. RESULTS: Data from 469 patients were included in the derivation cohort and 180 in the validation cohort. Admission haemoglobin OR 1.07(95% CI 1.06-1.08) and male gender OR 2.29(95% CI 1.40-3.77) predicted adverse outcomes in the derivation cohort AUC 0.86(95% CI 0.82-0.90) which outperformed the Blatchford 0.81(95% CI 0.77-0.85), Rockall 0.60(95% CI 0.55-0.65) and AIM65 0.55(0.50-0.60) scores and in the validation cohort AUC 0.80(95% CI 0.73-0.87) which outperformed the Blatchford 0.77(95% CI 0.70-0.85), Rockall 0.67(95% CI 0.59-0.75) and AIM 65 scores 0.61(95% CI 0.53-0.69). The Birmingham Score also performs well at predicting adverse outcomes from diverticular bleeding AUC 0.87 (95% CI 0.75-0.98). A score of 7 predicts a 94% probability of adverse outcome. CONCLUSION: The Birmingham Score represents a simple risk stratification score that can be used promptly on patients admitted with LGIB.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".