Abuse-deterrent formulations and opioid-related harms in North Carolina, 2010-2018
Bibliographic record
Abstract
Abuse-deterrent formulations of opioid analgesics (ADFs) were introduced to reduce opioid-related harms among pain patients, but postmarketing study results have been mixed. However, these studies may be subject to bias from selection criteria, comparator choice, and potential confounding by "indication," highlighting the need for thorough study design considerations. In a sample of privately insured patients prescribed ADF or non-ADF extended-release/long-acting (ER/LA) opioids in North Carolina, we implemented a version of the prevalent new-user design to evaluate the relationship between ADFs and opioid use disorder (OUD, n = 235) and opioid overdose (n = 18) through 6 months of follow-up using inverse probability-weighted cumulative incidence functions and Fine-Gray models. The weighted hazard ratio (HRw) of opioid overdose among patients initiating ADFs was 0.87 (95% CI, 0.23-3.24) times as high as among patients who initiated, restarted, or continued non-ADF ER/LA opioids. We observed a short-term benefit of ADFs for incident OUD (HRw = 0.58; 95% CI, 0.35-0.93) compared to non-ADF ER/LA opioids in the first 6 weeks of follow-up, but this benefit disappeared later in follow-up (HRw = 1.30; 95% CI, 0.86-1.95). In summary, our findings add to the expanding body of evidence that there is no clear long-term reduction in harm from ADF opioids among patients in outpatient use. This article is part of a Special Collection on Pharmacoepidemiology.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".