Characterizing dose changes and tapering among opioid users: A brief report on a population-level study in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: Monitoring changes in oral morphine equivalents (OMEs) is an important parameter to understand how opioids are being used at the population level. However, changes in opioid doses and tapering have not been well defined. DESIGN: We conducted a population-based exploratory data analysis (EDA) to characterize changes in opioid doses and tapering of opioids among patients in Alberta (AB). A literature review was conducted to assess opioid tapering. SETTING: Using dispense data from 2020 to 2021 provided by the College of Physicians & Surgeons of Alberta (CPSA), we assessed changes in OME per day from baseline to the subsequent quarter. PATIENTS: Patients living in AB. INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: The absolute and relative changes in OME per day were estimated for each assessment. Tapering was considered if an opioid user's OME per day changed from the baseline to zero in the subsequent quarter. The frequency and percentages of patients with different levels of changes in OME per day were summarized per quarter. RESULTS: There were 13 operational definitions of opioid tapering in the literature. Comparatively, our approach at the CPSA differed in the length of the follow-up assessment period. Based on our quarterly assessment of ~390,000 patients, all four periods showed 60 percent of patients had an opioid dose decrease/tapered therapy relative to baseline. However, 21 percent were noted to be new users of opioids. CONCLUSIONS: Based on our approach at the CPSA, 60 percent of patients tapered opioids over a year. Despite no standardized definition of opioid tapering, our EDA demonstrates one approach using population-based drug dispense data to evaluate opioid use.
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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 | 0.000 | 0.000 |
| 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".