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 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".