Predictors of inappropriate utilization of intravenous proton pump inhibitors
Bibliographic record
Abstract
BACKGROUND: Inappropriate use of intravenous proton pump inhibitors is prevalent. AIM: To assess appropriateness of intravenous proton pump inhibitor prescribing. METHODS: Retrospective review of in-patient prescribing of intravenous pantoprazole over a 2-month period in 2004, in an academic centre. Prescribing was deemed appropriate before and after endoscopic haemostasis, and in fasting individuals requiring a proton pump inhibitor. RESULTS: Amongst 107 patients, 49 (46%) had upper gastrointestinal bleeding. Overall, 33 (31%, 95% CI: 22-41%) received appropriate therapy (indication, dose and duration), 61 (57%, 95% CI: 47-67%) had an inappropriate indication, and 13 (12%, 95% CI: 7-20%) had an incorrect treatment dose or duration. Therapy was appropriate in 20 (41%, 95% CI: 27-55%) with upper gastrointestinal bleeding, and 13 (22%, 95% CI: 12-33%) in the non-upper gastrointestinal bleeding group. Appropriate prescribing rates decreased (from 41% to 16%, 95% on difference CI: 14-38%) when considering intravenous proton pump inhibitor use while awaiting endoscopy as inappropriate. Significant predictors of inappropriate use were increasing age and decreasing mean daily dose, with a trend for prescriptions written during evening shifts. CONCLUSION: Inappropriate intravenous proton pump inhibitor utilization was most frequent in the non-upper gastrointestinal bleeding group, mostly for unrecognized indications. Educational interventions to optimize utilization should target prescribing in older patients, those receiving lower mean daily doses, and, perhaps, prescribing outside regular hours.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".