Cross-sectional study of rapid tapering of opioid prescriptions following medical regulatory intervention in Alberta from 2013 to 2020
Bibliographic record
Abstract
OBJECTIVE: To determine if inappropriate tapering/discontinuation of opioids to Alberta patients occurred from mid-2013-2020, as unintended consequences of prescribing guidelines, regulations and policies in response to the North American opioid crisis. DESIGN: A population-based, repeated cross-sectional time-series study. SETTING: Alberta, Canada. PARTICIPANTS: Residents of Alberta, Canada aged 18 and older who received an opioid dispense from a community pharmacy from 2013 to 2020. MAIN OUTCOME MEASURES: The prevalence of potential rapid tapering was measured at a given date (reference day), enveloped by a data window. Dose changes were measured as oral morphine equivalents (OME) per patient, at multiple time points ('data window' around a reference day). Chronic recipients were identified, and their prescriptions were contrasted 90 days before and after the reference day to measure OME/day changes. RESULTS: Approximately 9000 dispenses (totalling ~6 million OME) per day were analysed from 2013 to 2020. The total number of opioid recipients was highly cyclic in nature (peaking in winter). The number of chronic opioid recipients remained somewhat stable from ~70K in 2013 to ~86K at the end of 2020. The number of chronic high and very high dose recipients presented a significant decrease after 2017. Approximately 11%-12% of chronic high-dose recipients experienced potential rapid dose tapering at a rate of 50% or more prereference to postreference day at any given point of time. For chronic very high dose recipients, approximately 11.5% experience potential rapid dose tapering at a rate of 50% or more prereference to postreference day at any given point of time. Potential discontinuation remained constant and the interventions did not have a significant impact on the trend. CONCLUSION: The evidence suggests that changes in prescribing guidelines were not associated with an increase of rapid opioid tapering/discontinuation in Alberta.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".