Peer-led Education Expedites De-Prescribing Proton Pump Inhibitors for Appropriate Veterans
Bibliographic record
Abstract
Costly proton pump inhibitors (PPIs) have been widely prescribed since the 1990’s for prevention and treatment of ulcers and gastroesophageal reflux disease (GERD). Evidence published since 2012 demonstrates risks associated with taking PPIs longer than eight weeks. Primary care providers (PCPs) mostly de-prescribe PPIs for persons not meeting criteria for long term use. Many patients resist discontinuation. A three-month evidenced based practice (EBP) education project was conducted by a nurse practitioner to improve PCP peer de-prescribing successes with appropriate patients in an outpatient California based veteran Primary Care Clinic. Fifteen PCPs were pretested about usual care practices between two comparable clinics. Five PCPs at the smaller clinic location were educated about long term PPI use risks and introduced to three EBP guidelines using tapering techniques with follow up care. A Canadian 2017 EBP PPI de-prescribing guideline was proposed for translation into practice. PCPs voted to pilot this guideline, dependent upon nursing support. PCPs denied frustration with usual care practices, even as all were willing to try an EBP practice change between pre and post-tests. Support for peer led EBP on site coaching increased from 87% to 100%. Tapering behavior increased from 67% to 100%, expediting improved long-term medication cessation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".