Reducing prescribing of benzodiazepines in older adults: a comparison of four physician-focused interventions by a medical regulatory authority
Bibliographic record
Abstract
BACKGROUND: The inappropriate and/or high prescribing of benzodiazepine and 'Z' drugs (BDZ +) is a major health concern. The purpose of this study was to determine whether physician or pharmacist led interventions or a simple letter or a personalized prescribing report from a medical regulatory authority (MRA) was the most effective intervention for reducing BDZ + prescribing by physicians to patients 65 years of age or older. METHODS: quarter of 2016. All physician-participants were sent a personalized prescribing profile by the MRA. They were then randomized into four groups that received either nothing more, an additional personal warning letter from the MRA, a personal phone call from an MRA pharmacist or a personal phone call from an MRA physician. The main outcomes were prescribing behavior change of physicians at one year in terms of: change in mean number of older patients receiving 4 + DDD BDZ + and mean dose BDZ + prescribed per physician. To adjust for multiple statistical testing, we used MANCOVA to test both main outcome measures simultaneously by group whilst controlling for any baseline differences. RESULTS: All groups experienced a significant fall in the total number of older patients receiving 4 + DDD of BDZ + by about 50% (range 43-54%) per physician at one year, and a fall in the mean dose of BDZ + prescribed of about 13% (range 10-16%). However, there was no significant difference between each group. CONCLUSIONS: A personalized prescribing report alone sent from the MRA appears to be an effective intervention for reducing very high levels of BDZ + prescribing in older patients. Additional interventions by a pharmacist or physician did not result in additional benefit. The intervention needs to be tested further on a more general population of physicians, prescribing less extreme doses of BDZ + and that looks at more clinical and healthcare utilization outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".