Opioid Prescriptions in Canadian Workers’ Compensation Claimants
Bibliographic record
Abstract
STUDY DESIGN: Historical cohort study. OBJECTIVE: We investigated the prescription of opioids in injured Canadian workers to determine recent trends in use and the association between early prescription and future recovery. SUMMARY OF BACKGROUND DATA: Opioid analgesia is effective for reducing chronic nonmalignant pain, and opioid prescriptions for musculoskeletal pain seem to have increased over the past years. However, recent evidence indicates early opioid use may be associated with delayed recovery in patients with back pain. METHODS: Data were extracted from the Alberta Workers' Compensation Board administrative database, and information was obtained on all time loss claims for sprains, strains, fractures, dislocations, amputations, or burns between January 1, 2000 and December 31, 2005. Information on all narcotic prescriptions was obtained along with demographic data and duration of time loss benefits. Injury severity was controlled for via nature of injury coding. Analysis included multivariable logistic and Cox regression. RESULTS: Data were obtained for 137,175 subjects. The majority were males ( approximately 70%) with back sprains (approximately 35%), and a mean age of 37 years. Between the years 2000 and 2005, all opioid prescriptions within the first year of claim decreased from 11.4% of claimants to 8.3%. Older males with fractures, dislocations, or amputations were more likely to receive narcotics. Claimants receiving early opioid prescriptions experienced delayed suspension of benefits. However, this association was also seen in claimants prescribed early non-narcotic analgesics. DISCUSSION: Prescriptions for opioid analgesia appear to be decreasing within workers' compensation claimants in Alberta, Canada. As expected, claimants with more severe injuries were more likely to receive opioids. An association was observed between early opioid prescription and delayed recovery, however, this is likely explained by pain severity or other unmeasured confounders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".