Predictors of a Variceal Source among Patients Presenting with Upper Gastrointestinal Bleeding
Bibliographic record
Abstract
BACKGROUND: Patients with upper gastrointestinal bleeding (UGIB) require an early, tailored approach best guided by knowledge of the bleeding lesion, especially a variceal versus a nonvariceal source. OBJECTIVE: To identify, by investigating a large national registry, variables that would be predictive of a variceal origin of UGIB using clinical parameters before endoscopic evaluation. METHODS: A retrospective study was conducted in 21 Canadian hospitals during the period from January 2004 until the end of May 2005. Consecutive charts for hospitalized patients with a primary or secondary discharge diagnosis of UGIB were reviewed. Data regarding demographics, including historical, physical examination, initial laboratory investigations, endoscopic and pharmacological therapies administered, as well as clinical outcomes, were collected. Multivariable logistic regression modelling was performed to identify clinical predictors of a variceal source of bleeding. RESULTS: The patient population included 2020 patients (mean [± SD] age 66.3±16.4 years; 38.4% female). Overall, 215 (10.6%) were found to be bleeding from upper gastrointestinal varices. Among 26 patient characteristics, variables predicting a variceal source of bleeding included history of liver disease (OR 6.36 [95% CI 3.59 to 11.3]), excessive alcohol use (OR 2.28 [95% CI 1.37 to 3.77]), hematemesis (OR 2.65 [95% CI 1.61 to 4.36]), hematochezia (OR 3.02 [95% CI 1.46 to 6.22]) and stigmata of chronic liver disease (OR 2.49 [95% CI 1.46 to 4.25]). Patients treated with antithrombotic therapy were more likely to experience other causes of hemorrhage (OR 0.44 [95% CI 0.35 to 0.78]). CONCLUSION: Presenting historical and physical examination data, and initial laboratory tests carry significant predictive ability in discriminating variceal versus nonvariceal sources of bleeding.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".