Changes in oxycodone and heroin exposures in the National Poison Data System after introduction of extended‐release oxycodone with abuse‐deterrent characteristics
Bibliographic record
Abstract
PURPOSE: Abuse and misuse of prescription opioids are serious public health problems. Abuse-deterrent formulations are an intervention to balance risk mitigation with appropriate patient access. This study evaluated the effects of physicochemical barriers to crushing and dissolving on safety outcomes associated with extended-release oxycodone (ERO) tablets (OxyContin) using a national surveillance system of poison centers. Other single-entity (SE) oxycodone tablets and heroin were used as comparators and to assess substitution effects. METHODS: The National Poison Data System covering all US poison centers was used to measure changes in exposures in the year before versus the 2 years after introduction of reformulated ERO (7/2009-6/2010 vs 9/2010-9/2012). Outcomes included abuse, therapeutic errors affecting patients, and accidental exposures. RESULTS: After ERO reformulation, abuse exposures decreased 36% for ERO, increased 20% for other SE oxycodone, and increased 42% for heroin. Therapeutic errors affecting patients decreased 20% for ERO and increased 19% for other SE oxycodone. Accidental exposures decreased 39% for ERO, increased 21% for heroin, and remained unchanged for other SE oxycodone. During the study period, other interventions to reduce opioid abuse occurred, for example, educational and prescription monitoring programs. However, these have shown small effects and do not explain a drop for ERO exposures but not for other opioids. CONCLUSIONS: After ERO reformulation, calls to poison centers involving abuse, therapeutic errors affecting patients, and accidental exposures decreased for ERO, but not for comparator opioids. Abuse-deterrent formulations of opioid analgesics can reduce abuse, but switching to other accessible non abuse-deterrent opioids might occur.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".