ER/LA Opioid Analgesics REMS: Overview of Ongoing Assessments of Its Progress and Its Impact on Health Outcomes
Bibliographic record
Abstract
Objective: Opioid abuse is a serious public health concern. In response, the Food and Drug Administration (FDA) determined that a risk evaluation and mitigation strategy (REMS) for extended-release and long-acting (ER/LA) opioids was necessary to ensure that the benefits of these analgesics continue to outweigh the risks. Key components of the REMS are training for prescribers through accredited continuing education (CE), and providing patient educational materials. Methods: The impact of this REMS has been assessed using diverse metrics including evaluation of prescriber and patient understanding of the risks associated with opioids; patient receipt and comprehension of the medication guide and patient counseling document; patient satisfaction with access to opioids; drug utilization and changes in prescribing patterns; and surveillance of ER/LA opioid misuse, abuse, overdose, addiction, and death. Results and Conclusions: The results of these assessments indicate that the increasing rates of opioid abuse, addiction, overdose, and death observed prior to implementation of the REMS have since leveled off or started to decline. However, these benefits cannot be attributed solely to the ER/LA opioid analgesics REMS since many other initiatives to prevent abuse occurred contemporaneously. These improvements occurred while preserving patient access to opioids as a large majority of patients surveyed expressed satisfaction with their access to opioids.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".