Defining opioid naïve and implications for monitoring opioid use: A population-based study in Alberta, Canada
Bibliographic record
Abstract
Purpose: Reducing initial exposure of “opioid naïve” patients to opioids is a public health priority. Identifying opioid naïve patients is difficult, as numerous definitions are used. The objective is to summarize current definitions and evaluate their impact on opioid naïve measures in Alberta. Methods: Using dispense data (2017-2021) and definitions guided by a scoping review, we determined the number of “opioid naïve” patients using descriptive analyses. Three definitions were identified: 1) no opioid use within the previous 30 days/6 months/1 year, based on dispensation date; 2) definition 1, based on dispensation date plus days of supply; 3) exclusion of codeine from definitions 1 and 2. Results: Of over a dozen definitions of opioid naïve identified in the scoping review, most used an ‘opioid free’ period (commonly 30 days/6 months/1 year). Other definitions included “availability of drug” based on days of supply and/or excluded certain opioid products. Approximately 36.4% of Albertans (n=1,551,075) had an opioid dispensation in 2017-2021. The average age was 46.6±18.8 and 52.8% were female. Results were most affected by the “opioid free” period, with 97.4%, 83.2% and 65.6% being classified as opioid naïve using time windows from definition 1. Definitions 2 and 3 did not materially change the results. Conclusions: The most convenient definition for “opioid naïve” was definition 1 using a 1-year window, which aligns with the Canadian Institutes for Health Information definition. Irrespective of definition used, a large proportion of opioid users would be considered opioid naïve despite initiatives to curb opioid prescription 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".