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: An exploratory data analysis of the literature was conducted over the last 10 years to identify definitions commonly used in the literature to define opioid naïve. Then, using these definitions as a guide, we descriptively report the proportion of patients in Alberta between 2017 and 2021 who would be considered as opioid naïve using these definitions and all opioid dispensing data. RESULTS: Three categories of definitions were broadly identified: (1) no opioid use within the previous 30 days/6 months/1 year, based on dispensation date; (2) no opioid use based on dispensation date plus days of supply; and, (3) exclusion of codeine from Definitions 1 and 2. Applying these definitions to the Alberta population showed a very wide range in the proportion who would be considered as opioid naïve. Overall, 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. The proportion of opioid naïve 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 (30 days, 6 months, 1 year of no prior opioid use). Definitions 2 and 3 did not materially change the results. Further extending the "opioid free" period to 2 years showed only 35% were opioid naïve. CONCLUSIONS: The most convenient definition for "opioid naïve" was the use of an "opioid free" period. The choice of window would depend on how the information may be used to assistant in clinical decisions with longer windows more likely to reflect true opioid naïve patients. Irrespective of definition used, a large proportion of opioid users would be considered opioid naïve in Alberta.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".