Non-Medical Use of Prescription Analgesics: A Three-Year National Longitudinal Study
Bibliographic record
Abstract
This secondary analysis of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data examined the non-medical use of prescription analgesics and determined its relationship to continued non-medical use and substance use disorders 3 years later. Prospective data were collected using the Alcohol Use Disorders and Associated Disabilities Interview Schedule: DSM-IV Version (AUDADIS-DSM-IV). A nationally representative sample (n = 34,653) of U.S. adults 18 years or older were interviewed at Wave 1 (2001-2002) and re-interviewed at Wave 2 (2004-2005). Multivariate logistic regression analyses indicated younger age (18 to 24 years) and non-medical use at Wave 1 was associated with higher odds of a general substance or opioid use disorder at Wave 2 (adjusted odds ratio = 3.42, 95% confidence interval = 1.45, 8.07); however, most respondents who engaged in non-medical use will cease using 3 years later although non-medical use is associated with higher prevalence of a future substance use disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".