Can the Presence of Endoscopic High-risk Stigmata Be Confidently Predicted before Endoscopy: A Multivariable Analysis Using the RUGBE Database
Bibliographic record
Abstract
Purpose: Many aspects in the management of acute upper gastrointestinal bleeding (UGIB) rely on preesophagogastroduodenoscopy (EGD) stratification of patients likely to exhibit high-risk stigmata (HRS); however, data predicting the presence of HRS are lacking.The purpose of this study is to determine clinical and laboratory predictors of HRS at index EGD in patients presenting with acute UGIB, using a retrospective data of a validated national database: the Canadian RUGBE registry. Methods: We evaluated relevant clinical and laboratory parameters. HRS was defined as a spurting, oozing, non-bleeding visible vessel or adherent clot after vigorous irrigation. Multivariable modeling was used to identify predictors of HRS, including age, gender, hematemesis, use of antiplatelet agents (APA), American Society of Anesthesiologists (ASA) classification, nasogastric (NG) tube aspirate, hemoglobin level and elapsed time from onset of bleeding to EGD. Results: Of the 1,677 patients (66.2±16.8 yrs; 38.3% female), 28.7% had hematemesis, 57.8% had an ASA score of 3 to 5, and mean hemoglobin 9.7±2.7 g/dL. Mean time from presentation to endoscopy was 22.2±37.5 hours. The best-fitting multivariable model included the following significant predictors (odds ratios; 95% CI estimates): ASA score 3 to 5 (2.16; 95% CI 1.71, 2.74), a shorter time to endoscopy (0.99; 95% CI 0.98, 0.99) and a lower initial hemoglobin (0.99; 95% CI 0.99, 0.99). Conclusion: A higher ASA score, a shorter time to endoscopy, and lower initial hemoglobin level all significantly predict the presence of endoscopic HRS. These criteria could potentially 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".