Physicians’ approaches to the use of gastroprotective strategies in low‐risk non‐steroidal anti‐inflammatory drug users
Bibliographic record
Abstract
BACKGROUND: Many doctors unnecessarily prescribe gastroprotective strategies to non-steroidal anti-inflammatory drugs users at low risk of non-steroidal anti-inflammatory drug-related gastrointestinal complications. AIM: To identify factors that predict the overuse of gastroprotective strategies in low-risk non-steroidal anti-inflammatory drug users. METHODS: We distributed a questionnaire to family doctors and general internists consisting of a clinical vignette describing a low-risk hypothetical patient with osteoarthritis who was a candidate for non-steroidal anti-inflammatory drug therapy. Respondents were asked whether they would prescribe this patient a gastroprotective strategy and to estimate the annual risk of that patient developing a gastrointestinal complication with non-steroidal anti-inflammatory drug use. Respondents inappropriately recommending a gastroprotective strategy were compared with respondents who opted not to use gastroprotection. RESULTS: We received 340 responses (response rate of 28.3%), of which 278 supplied analysable data. Thirty-five percent of respondents inappropriately recommended a gastroprotective strategy for the low-risk subject. Inappropriate prescribers were significantly more likely to overestimate the risk of gastrointestinal complications with traditional non-steroidal anti-inflammatory drugs and this was strongly predictive of gastroprotective strategy recommendation in logistic regression. CONCLUSIONS: Many doctors inappropriately recommend gastroprotective strategies in low-risk non-steroidal anti-inflammatory drug users. Improving doctors' awareness of non-steroidal anti-inflammatory drug-associated gastrointestinal risks may lead to a decrease in inappropriate utilization of gastroprotective strategies in low-risk patients.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".