Can the Presence of Endoscopic High-Risk Stigmata be Bredicted before Endoscopy? A Multivariable Analysis Using the RUGBE Database
Bibliographic record
Abstract
BACKGROUND: Many aspects in the management of acute upper gastrointestinal bleeding rely on pre-esophagogastroduodenoscopy (EGD) stratification of patients likely to exhibit high-risk stigmata (HRS); however, data predicting the presence of HRS are lacking. OBJECTIVE: To determine clinical and laboratory predictors of HRS at the index EGD in patients presenting with acute upper gastrointestinal bleeding using retrospective data from a validated national database - the Canadian Registry in Upper Gastrointestinal Bleeding and Endoscopy registry. methods: Relevant clinical and laboratory parameters were evaluated. HRS was defined as spurting, oozing, nonbleeding visible vessel or adherent clot after vigorous irrigation. Multivariable modelling was used to identify predictors of HRS including age, sex, hematemesis, use of antiplatelet agents, American Society of Anesthesiologists (ASA) classification, nasogastric tube aspirate, hemoglobin level and elapsed time from the onset of bleeding to EGD. RESULTS: Of the 1677 patients (mean [± SD] age 66.2 ± 16.8 years; 38.3% female), 28.7% had hematemesis, 57.8% had an ASA score of 3 to 5, and the mean hemoglobin level was 96.8 ± 27.3 g⁄L. The mean time from presentation to endoscopy was 22.2 ± 37.5 h. The best fitting multivariable model included the following significant predictors: ASA score 3 to 5 (OR 2.16 [95% CI 1.71 to 2.74]), a shorter time to endoscopy (OR 0.99 [95% CI 0.98 to 0.99]) and a lower initial hemoglobin level (OR 0.99 [95% CI 0.99 to 0.99]). CONCLUSION: A higher ASA score, a shorter time to endoscopy and lower initial hemoglobin level all significantly predicted the presence of endoscopic HRS. These criteria could be used to improve the optimal selection of patients requiring more urgent endoscopy.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".