Red Blood Cell Transfusions and Iron Therapy for Patients Presenting with Acute Upper Gastrointestinal Bleeding: A Survey of Canadian Gastroenterologists and Hepatologists
Bibliographic record
Abstract
Introduction. There is limited data evaluating physician transfusion practices in patients with acute upper gastrointestinal bleeding (UGIB). Methods. A web-based survey was sent to 500 gastroenterologists and hepatologists across Canada. The survey included clinical vignettes where physicians were asked to choose transfusion thresholds. Results. The response rate was 41% (N = 203). The reported hemoglobin (Hgb) transfusion trigger differed by up to 50 g/L. Transfusions were more liberal in hemodynamically unstable patients compared to stable patients (mean Hgb of 86.7 g/L versus 71.0 g/L; p < 0.001). Many clinicians (24%) reported transfusing a hemodynamically unstable patient at a Hgb threshold of 100 g/L and the majority (57%) are transfusing two units of RBCs as initial management. Patients with coronary artery disease (mean Hgb of 84.0 g/L versus 71.0 g/L; p < 0.01) or cirrhosis (mean Hgb of 74.4 g/L versus 71.0 g/L; p < 0.01) were transfused more liberally than healthy patients. Fewer than 15% would prescribe iron to patients with UGIB who are anemic upon discharge. Conclusions. The transfusion practices of gastroenterologists in the management of UGIB vary widely and more high-quality evidence is needed to help assess the efficacy and safety of selected transfusion thresholds in varying patients presenting with UGIB.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".