Opioid deprescribing: rethinking policies to facilitate better patient outcomes
Bibliographic record
Abstract
Deprescribing, the patient-centered process of reducing or stopping a medication when the potential harms outweigh the likely benefits, has emerged as a promising strategy to mitigate opioid-related harm. Typically, opioid deprescribing occurs at the individual level, however, adopting a policy-driven approach could expand its reach and impact. To date, prescription opioid control policies that have been implemented with the intention of reducing opioid use and harm have often resulted in unintended consequences. In this article we discuss whether and how the concept of opioid deprescribing can be operationalized at a policy level. We review the goals, challenges and consequences of opioid control policies, explore how they intersect with system-level factors, and propose pathways for developing and implementing future opioid deprescribing policies. We argue that the development and implementation of patient-centered opioid deprescribing policies are both essential and feasible, if key challenges such as structural stigma and the complex interplay between pain and opioid use disorder are recognized and addressed. Robust evaluation frameworks will also be critical for monitoring outcomes and refining interventions. By prioritizing patient and provider needs, and carefully considering pertinent system-level factors, policymakers may be able to foster more effective and compassionate opioid management and reduce opioid-related harm.
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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 | 0.011 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".