Determining the influence of the Workers Compensation Board of Manitoba's opioid policy on prescription opioid use amongst WCB recipients
Bibliographic record
Abstract
BACKGROUND: Opioid medications are commonly used by Workers Compensation Board (WCB) claimants following workplace injuries. The purpose of this study is to describe the impact of an opioid management policy on opioid prescriptions amongst a WCB-covered population compared to changes in the use of these medications in the general population of a Canadian province. METHODS: We linked WCB claims data from 2006 to 2016 (13,155 claims, 11,905 individuals) to Manitoba provincial health records and compared opioid use amongst this group to 478,606 individuals aged 18-65. Linear regression was performed to examine the change over time in number of individuals being prescribed opioids for various durations and dosages of 50 or more, and 120 or more morphine equivalents (ME)/day for both the WCB and Manitoba population. RESULTS: WCB claimants totaled 2.5% of Manitoba residents aged 18-65 who were prescribed opioids for non-cancer pain. After the introduction of the opioid use policy for the WCB population in November 2011, the number of people prescribed opioids declined 49.4% in the WCB group, while increasing 10.8% in the province as a whole. The number of individuals using 50 ME/day or more declined 43.1% in the WCB group and increased 5.8% in the province. CONCLUSIONS: Opioid management programs organized by a compensation board can lead to a substantial reduction in the prescription of opioid medications to a WCB client population, including individuals who were prescribed higher doses of these medications when compared with general trends in the community.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".