Opioid deprescribing: rethinking policies to facilitate better patient outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it