Do abuse deterrent opioid formulations work?
Bibliographic record
Abstract
OBJECTIVE: We performed a systematic review to answer the question, "Does the introduction of an opioid analgesic with abuse deterrent properties result in reduced overall abuse of the drug in the community?" DESIGN: We included opioid analgesics with abuse deterrent properties (hydrocodone, morphine, oxycodone) with results restricted to the metasearch term "delayed onset," English language, use in humans, and publication years 2009-2016. All articles that contained data evaluating misuse, abuse, overdose, addiction, and death were included. The results were categorized using the Bradford-Hill criteria. RESULTS: We included 44 reports: hydrocodone (n = 7), morphine (n = 5), or oxycodone (n = 32) with Food and Drug Administration-approved Categories 1, 2, or 3 abuse deterrent labeling. The data currently available support the Hill criteria of strength (effect size), consistency (reproducibility), temporality, plausibility, and coherence. There was insufficient or no information available for the criteria of biological gradient, experiment, and analogy. We also assessed confounding factors and bias, which indicated that both were present and substantial in magnitude. CONCLUSIONS: Our analysis found that only oxycodone extended release (ER) had information available to evaluate abuse deterrence in the community. In Australia, Canada, and the United States, reformulation of oxycodone ER was followed by marked reduction in measures of abuse. The precise extent of reduced abuse cannot be calculated because of heterogeneous data sets, but the reported reductions ranged from 10 to 90 percent depending on the measure and the duration of follow-up.
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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 | 0.027 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".