Bibliographic record
Abstract
RATIONALE AND AIMS: Evidence-based medicine (EBM) has gained worldwide attention. Many studies have used questionnaires to discuss factors obstructing the practice of EBM. However, no large-scale data analysis has focused on who has practised EBM and when they practised it. This retrospective study aims to fill the research gap by applying nationally representative data to analyse EBM practice after the provision of new evidence regarding the prescription of rosiglitazone which has been shown to increase the risk of myocardial infarction. METHODS: We used the National Health Insurance Database in Taiwan to analyse the variations in rosiglitazone prescription among physicians. The study period was from the second quarter of 2007 to the fourth quarter of 2008. A total of 2536 physicians who prescribed rosiglitazone at least once were included in this study. We applied multivariate logistic analyses to predict the probability of physicians ceasing to prescribe rosiglitazone. RESULTS: We observed a significant improvement in EBM practice among specialists and experienced physicians. Endocrinologists were four times more likely to change rosiglitazone prescription habits than other specialists (odds ratio 4.129, 95% confidence interval 2.484-6.863). Doctors with more than 10 years of specialist experience performed better in EBM practice. Moreover, a prominent time lag with more than 6 months between EBM emergence and EBM practice was noticed. CONCLUSIONS: Our study suggested that EBM was still not well practised, using rosiglitazone prescription as a study case. Further education and encouragement to strengthen physicians' EBM practice remain urgently needed within the medical community.
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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 | 0.038 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".