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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".