How it begins: Initial response to opioids strongly predicts self-reported opioid use disorder
Bibliographic record
Abstract
Background Opioid use disorder (OUD) is a major public health crisis. Patients' initial exposure to opioids often comes from prescribed medications. Predicting which of these patients will develop OUD remains challenging. Prior evidence from various substances suggest that initial subjective responses influence addiction risk, however these studies have used relatively small cohorts and have not led to the development of widespread tools to predict OUD risk. Methods We used a cohort of 141,897 adult research participants to perform a retrospective observational study of self-reported subjective responses to prescription opioids. We collected demographics, subjective positive (e.g., euphoria), subjective negative (e.g., nausea), and analgesic responses as well as self-reported OUD. Results Positive subjective effects, particularly "Like Overall", "Euphoric", and "Energized", were the strongest predictors of OUD. For example, the odds-ratio for individuals responding "Extremely" for "Like Overall" was 36.5. The sensitivity and specificity of this single question was excellent (ROC=0.87). Negative effects and analgesic effects were much less predictive. We developed a two-question decision tree ("When you first took opioid pain medication, to what extent did you like the way they made you feel overall?" and "When you first took opioid pain medication, to what extent did you experience an unpleasant itchy feeling?"), that can identify a small high-risk subset with 78.5% prevalence of OUD and a much larger low-risk subset with 1.2% prevalence of OUD. Conclusions Screening for subjective responses can identify high-risk individuals who would benefit from tailored interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".