Changes in early high‐risk opioid prescribing practices after policy interventions in Washington State
Bibliographic record
Abstract
OBJECTIVE: To test associations between several opioid prescribing policy interventions and changes in early (acute/subacute) high-risk opioid prescribing practices. DATA SOURCES: Population-based workers' compensation pharmacy billing and claims data, Washington State Department of Labor and Industries (January 2008-June 2015). STUDY DESIGN: We used interrupted time series analysis to test associations between three policy intervention timepoints and monthly proportions of population-based measures of high-risk, low-risk, and any workers' compensation-related opioid prescribing. We also tested associations between the policy intervention timepoints and five high-risk opioid prescribing indicators among workers prescribed any opioids within 3 months after injury: (a) >7 cumulative (not necessarily consecutive) days' supply of opioids during the acute phase, (b) high-dose opioids, (c) concurrent sedatives, (d) chronic opioids, and (e) a composite high-risk opioid prescribing indicator. PRINCIPAL FINDINGS: Within 3 months after injury, 9 percent of workers were exposed to high-risk and 12 percent to low-risk workers' compensation-related opioid prescribing; 79 percent filled no workers' compensation-related opioid prescription. Among workers prescribed any early (acute/subacute) opioids, the indicator for >7 days' supply of opioids during the acute phase was present for 30 percent, high-dose opioids for 18 percent, concurrent sedatives for 3 percent, and chronic opioids for 2 percent. Beyond a general shift toward more infrequent and lower-risk workers' compensation-related opioid prescribing, each policy intervention timepoint was significantly associated with reductions in specific acute/subacute high-risk opioid prescribing indicators; each of the four specific high-risk opioid prescribing indicators had significant reductions associated with at least one policy. CONCLUSIONS: Several state-level opioid prescribing policies were significantly associated with safer workers' compensation-related opioid prescribing practices during the first 3 months after injury (acute/subacute phase), which should in turn reduce transition to chronic opioids and associated negative health outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".