The Glasgow Blatchford scoring system enables accurate risk stratification of patients with upper gastrointestinal haemorrhage
Bibliographic record
Abstract
BACKGROUND: Upper gastrointestinal (UGI) haemorrhage is a frequent cause of hospital admission. Scoring systems have been devised to identify those at risk of adverse outcomes. We evaluated the Glasgow Blatchford score's (GBS) ability to identify the need for clinical and endoscopic intervention in patients with UGI haemorrhage. METHODS: A retrospective observational study was performed in all patients who attended the A&E department with UGI haemorrhage during a 12-month period. Patients were separated into low and high risk categories. High risk encompassed patients who required blood transfusions, operative or endoscopic interventions, management on high dependency or intensive care units, and those who re-bled, represented with further bleeding, or who died. RESULTS: A total of 174 patients were seen with UGI bleeding. Eight of them self-discharged and were excluded. Of the remaining 166, 94 had a 'low risk' bleed, and 72 'high risk'. The GBS was significantly higher in the high risk (median = 10) than in the low risk group (median 1, p < 0.001). To assess the validity of the GBS at separating low and high risk groups, receiver-operator characteristic (ROC) curves were plotted. The GBS had an area under ROC curve of 0.96 (95% CI 0.95-1.00). When a cut-off value of > or = 3 was used, sensitivity and specificity of GBS for identifying high risk bleeds was 100% and 68%. Thus at a cut-off value of < or = 2 the GBS is useful for distinguishing those patients with a low risk UGI bleed. CONCLUSIONS: The GBS accurately identifies low risk patients who could be managed safely as outpatients.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".