High‐dose intravenous proton pump inhibition following endoscopic therapy in the acute management of patients with bleeding peptic ulcers in the USA and Canada: a cost‐effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: The efficacy of high-dose intravenous proton pump inhibition has recently been shown, yet its cost-effectiveness remains poorly studied. AIM: To assess the cost-effectiveness of this approach separately for American and Canadian health care settings. METHODS: A validated decision model included patients with bleeding ulcers after successful endoscopic haemostasis. Probabilities were determined from the literature, and charges and lengths of stay from national databases. A third-party payer perspective was adopted over a 30-day time horizon. RESULTS: Re-bleeding rates were 5.9% for patients who received high-dose intravenous proton pump inhibition and 22.9% for those who did not. Hospitalization costs for patients with and without re-bleeding were 11,802 US dollars and 7993 US dollars, and 5220 Canadian dollars and 2696 Canadian dollars, respectively. High-dose intravenous proton pump inhibition was more effective and less costly than the alternative of not administering it. The cost-effectiveness ratios for high-dose and no high-dose intravenous proton pump inhibition were 9112 US dollars and 11,819 US dollars (3293 dollars and 4284 dollars for the Canadian case), respectively. Sensitivity and threshold analyses showed that the results were robust across a wide range of clinically relevant assumptions. CONCLUSION: In the USA and Canada, administering high-dose intravenous proton pump inhibition for 3 days is both more effective and less costly than not doing so for patients with bleeding ulcers after successful endoscopic haemostasis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".