Rethinking ranitidine use in hospitals: how the ranitidine recall exposed a lack of evidence behind standardized hospital order sets
Bibliographic record
Abstract
ii. Rationale, aims and objectives Fraser Health, a large health authority, undertook an audit of standardized order sets (SOS) listing ranitidine due to the Health Canada recall of ranitidine. Our primary objective was to determine if ranitidine use on SOSs was supported by the best available evidence, in order to sparingly use ranitidine in the hospital. ii. Method Two evaluators recorded the indication of ranitidine on every SOS and a scoping review of systematic review evidence was conducted in parallel to a comprehensive review of evidence quality. Clinical practice guideline recommendations were also recorded in order to make comparisons to systematic review evidence. iii. Results Twenty-seven SOSs were found. Seven SOSs (26%) clearly indicated the medical condition ranitidine was being used for. Twenty SOSs (74%) did not list an indication or had an unclear indication. Six SOSs (22%) were supported by systematic review evidence: 4 intensive care unit (ICU) SOSs for stress ulcer prophylaxis, 1 nausea and vomiting of pregnancy SOS for heartburn, and 1 emergency department SOS for heartburn iv. Conclusion The SOS ranitidine audit conducted at Fraser Health has highlighted inconsistencies between institutional prescribing policies and evidence. Drugs listed on SOSs should be carefully considered before being used at an institutional level. To aid prescribers’ decision making, it may also be beneficial to indicate what the purpose of each drug is on a SOS Our team plans to use this as an opportunity to revise other ranitidine SOSs to reflect best evidence. Evaluation of how ranitidine or other drugs were being prescribed from SOSs is encouraged.
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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 | 0.316 | 0.643 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".