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 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 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".