Effect of a centralized prescription network on inappropriate prescriptions for opioid analgesics and benzodiazepines
Bibliographic record
Abstract
BACKGROUND: Opioid analgesics and benzodiazepines are often misused in clinical practice. We determined whether implementation of a centralized prescription network offering real-time access to patient-level data on filled prescriptions (PharmaNet) reduced the number of potentially inappropriate prescriptions for opioids and benzodiazepines. METHODS: We conducted a time series analysis using prescription records between Jan. 1, 1993, and Dec. 31, 1997, for residents of the province of British Columbia who were receiving social assistance or were 65 years or older. We calculated monthly percentages of filled prescriptions for an opioid or a benzodiazepine that were deemed inappropriate (those issued by a different physician and dispensed at a different pharmacy within 7 days after a filled prescription of at least 30 tablets of the same drug). RESULTS: Within 6 months after implementation of PharmaNet in July 1995, we observed a relative reduction in inappropriate filled prescriptions for opioids of 32.8% (95% confidence interval [CI] 31.0%-34.7%) among patients receiving social assistance; inappropriate filled prescriptions for benzodiazepines decreased by 48.6% (95% CI 43.2%-53.1%). Similar and statistically significant reductions were observed among residents 65 years or older. INTERPRETATION: The implementation of a centralized prescription network was associated with a dramatic reduction in inappropriate filled prescriptions for opioids and benzodiazepines.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| 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".