Internists’ Perceptions of Proton Pump Inhibitor Adverse Effects and Impact on Prescribing Practices: Results of a Nationwide Survey
Bibliographic record
Abstract
BACKGROUND: Observational studies have linked proton pump inhibitors (PPIs) with serious adverse effects. The study aimed to evaluate internists' perceptions of PPI harms and effects on prescribing. METHODS: This was an online survey of a representative sample of the American College of Physicians in 2013. We queried familiarity with and concern about PPI adverse effects (1 - 7 Likert-type scales, anchored by "not at all" and "extremely"). We also asked how frequently (often, sometimes, rarely, or never) participants used any of three "de-escalation" strategies to stop or reduce PPIs because of concern about adverse effects: reducing patients' PPI dose, switching to H2 blocker, or discontinuing PPI. We used multivariable logistic regression to evaluate associations between sometimes/often using any PPI de-escalation strategy and gender, time in practice, familiarity, and concern. RESULTS: The response rate was 53% (487/914). Seventy percent were male, median time in practice was 11 - 15 years, and most practiced general medicine (58%). Ninety-nine percent reported at least some familiarity with reported adverse effects (mean 4.9, standard deviation (SD) 1.0), and 98% reported at least some concern (mean 4.6, SD 1.3). Sixty-three percent reported sometimes/often reducing the PPI dose, 52% switching to H2 blocker, and 44% discontinuing PPI. In multivariable analysis, familiarity with adverse effects (OR 1.66 (1.31 - 2.10) for 1-point increase, P < 0.001) and concern (OR 2.14 (1.76 - 2.61) for 1-point increase, P < 0.001) were independently associated with de-escalation. Gender and time in practice had no effects. CONCLUSION: Almost all internists report awareness and concern about PPI adverse effects, and most are de-escalating PPIs as a result. Research on which approach is most effective for which patients is critically important.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".