National Trends in Prescription Opioid Risk Mitigation Practices: Implications for Prescriber Education
Bibliographic record
Abstract
OBJECTIVES: To assess national trends in selected prescription opioid risk mitigation practices and associations with prescriber type, state-specific opioid overdose severity, and required pain education. METHODS: Analysis of the national SCOPE of Pain registrants' baseline self-report of five safer opioid prescribing practices over three years (March 2013-Februrary 2016). RESULTS: Of 6,889 registrants for SCOPE of Pain, 70-94% reported performing each of five opioid risk mitigation practices for "most or all" patients, with 49% doing so for all five practices. Only 28% performed all five practices for "all" patients prescribed opioids. There were few differences among three yearly cohorts. Advanced practice nurses reported performing practices for "all" patients more often than physicians or physician assistants. Clinicians from states with high opioid overdose rates reported significantly higher implementation of most practices, compared with clinicians from states with low rates. CONCLUSIONS: Prescribers report low levels of employing five opioid risk mitigation practices for all patients prescribed opioids before attending a safer opioid prescribing training. POLICY IMPLICATIONS: Safer opioid prescribing education should transition from knowledge acquisition toward universal implementation of opioid risk mitigation practices.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".