Contemporary prescription opioid use for pain among Canadian Armed Forces Veterans in Ontario
Bibliographic record
Abstract
Ontario Veterans with an Opioid Prescription for Pain Ontario Veterans with a New Opioid Prescription for Pain Matched Ontario non-Veterans with an Opioid Prescription for Pain Matched Ontario non-Veterans with a New Opioid Prescription for Pain Percentage of people receiving an opioid prescription for pain (%) A slightly higher prevalence of opioid anal gesic use was found among Ontario Veterans compared to the matched Ontario non Veteran population, with rates beginning to decline in 2016 (16%) and falling to 12% by 2019 among Ontario Veterans.Trends in rates of new opioid use also declined in both groups over the study period and were similar in 2019 (7%).Tese fndings may refect the higher burden of chronic pain among Ontario Veterans; however, the apparent lack of differences in opioid initiation between Veterans and non Veterans from 2017 onwards may also refect growing reluctance to initiate opioids that has been associated with the publication of American and Canadian guidelines around this time.8,16
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".