Opioid abuse-deterrent strategies: role of clinicians in acute pain management
Bibliographic record
Abstract
Opioid abuse is a healthcare and societal problem that burdens individuals, their families and the healthcare professionals who care for them. Restricting access to opioid analgesics is one option to deter abuse, but this may prevent pain patients in need from obtaining effective analgesics. Therefore, strategies that mitigate the risk of opioid abuse while maintaining access are being pursued by several stakeholders including federal agencies, state governments, payors, researchers, the pharmaceutical industry and clinicians. Federal agency efforts have included required licensure and documentation for prescribing opioids, implementation of risk evaluation and mitigation strategies, and guidance on assessment and labeling of opioid abuse-deterrent formulations. In addition, state governments and payors have enacted monitoring programs, and pharmaceutical companies continue to develop abuse-deterrent opioid formulations. Strategies for clinicians to mitigate opioid abuse include comprehensive patient assessment and universal precautions (e.g. use of multimodal analgesia and abuse-deterrent opioid formulations, urine toxicology screening, participation in prescription drug monitoring and risk evaluation and mitigation strategy programs).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".