Effects of the new prescribing standards in British Columbia on consumption of opioids and benzodiazepines and <i>z</i> drugs.
Bibliographic record
Abstract
OBJECTIVE: drug prescribing standards on the use of these medications in British Columbia. DESIGN: Interrupted time-series analysis of community-prescribing records over a 30-month period: January 2015 to June 2017. SETTING: British Columbia. PARTICIPANTS: Random sample of British Columbia residents with filled prescriptions during the study period. INTERVENTION: drug prescribing standards on June 1, 2016. MAIN OUTCOME MEASURES: drugs (measured in diazepam equivalents); and total monthly users of each class of medication. RESULTS: drugs mirrored those seen for opioids for pain. CONCLUSION: drugs that began 6 months earlier. However, the standards did have a small effect on the number of monthly users of these medications, with a decrease in opioid prescribing among continuing users. Given the risk of destabilization of patients who are discontinued from opioid therapy, future research should assess how patient health outcomes are related to changing prescribing practices.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".