Effect of periodic letters on evidence-based drug therapy on prescribing behaviour: a randomized trial
Bibliographic record
Abstract
BACKGROUND: The effect of regular and expected printed educational materials on physician prescribing behaviour has not been studied. We sought to measure the impact of a series of evidence-based drug therapy letters mailed to physicians in British Columbia on prescribing to newly treated patients. METHODS: A paired, cluster randomized community design was used. The study population included 499 physicians from 24 local health areas in British Columbia. Local health areas were paired by number of physicians, and 1 of each pair was randomly selected and its physicians assigned to an intervention group or a control group. The intervention was 12 issues of an evidence-based series called Therapeutics Letter. Physicians in the control group (n = 241) received the letters 3-8 months after physicians in the intervention group (n = 258). The impact on prescribing to newly treated patients (defined as patients who had not previously made a claim for any medication from the class of drugs profiled in the letter) was analyzed using the drug claims database of BC Pharmacare, a publicly funded drug benefits program that covered all seniors and people receiving social assistance. RESULTS: The probability of prescribing a drug recommended in the Therapeutics Letter rather than another drug in the same class increased by 30% in the 3 months after the mailing of the letter relative to the preceding 3 months, adjusted for any before-after changes in the control group (relative risk 1.30; 95% confidence interval 1.13-1.52). No letter achieved statistical significance on its own. However, 11 of the 12 letters produced prescribing changes in the predicted direction such that the overall result was significant when their effect was combined. INTERPRETATION: The combined effect of an ongoing series of printed letters distributed from a credible and trusted source can have a clinically significant effect on prescribing to newly treated 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".