Educating physicians to reduce benzodiazepine use by elderly patients: a randomized controlled trial.
Bibliographic record
Abstract
BACKGROUND: Benzodiazepine use by elderly patients is associated with adverse outcomes including increased risk of falls and fractures, motor vehicle accidents and cognitive impairment. Recent studies suggest that individualized feedback and education to physicians may improve drug prescribing. In this study, we evaluated an intervention to address the inappropriate prescribing of benzodiazepines for elderly patients. METHODS: We identified 1624 primary care physicians who wrote at least 10 prescriptions for the target drugs in a 2-month period and randomly assigned these physicians to the intervention group or the control group. We obtained data from the Ontario Drug Benefit claims database, which covers all Ontario residents aged 65 years and over for drugs selected from a minimally restrictive formulary. Every 2 months for 6 months, confidential profiles of benzodiazepine prescription use coupled with evidence-based educational bulletins were mailed to the intervention group. The control group received feedback and educational bulletins about first-line antihypertension drug prescribing for elderly patients. Our main outcome measures were reductions in the proportion of each physician's total benzodiazepine prescriptions for long-acting agents, combinations of benzodiazepines with other psychoactive medications (including other benzodiazepines) and long-term benzodiazepine therapy. RESULTS: After randomization, 168 physicians agreed to be in the intervention group and 206 in the control group. Their demographic and prescribing characteristics were similar. Although the proportion of long-acting benzodiazepine prescriptions decreased by 0.7% in the intervention group between the baseline period and the end of the intervention period (from 20.3%, or a mean of 29.5 prescriptions, to 19.6%, or a mean of 27.7 prescriptions) and increased by 1.1% in the control group (from 19.8%, or a mean of 26.4 prescriptions, to 20.9%, or a mean of 27.7 prescriptions) (p = 0.036), this difference was not clinically significant. There was no significant difference over the study period in either combination prescribing of benzodiazepines or in prescriptions for long-term benzodiazepine therapy. INTERPRETATION: We did not find that a program of confidential feedback and educational material offered to Ontario primary care physicians had a clinically significant impact on their benzodiazepine prescribing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".