Associations between Concurrent Substance Use and Anabolic-Androgenic Steroid Use among Adolescents
Bibliographic record
Abstract
BACKGROUND: Concurrent use of e-cigarettes, cigarettes, and cannabis is common among adolescents, while illicit anabolic-androgenic steroid (AAS) use has recently been on the rise. Today, no known research has investigated the patterns of concurrent substance use and AAS use among adolescents in the United States. OBJECTIVE: To determine the association between concurrent lifetime use of e-cigarettes, cigarettes, and cannabis and illicit AAS use among adolescents. METHODS: = 13,677) were analyzed in 2021. Four mutually exclusive categories of concurrent substance use (no use, any single use, any dual use, and triple use) were constructed, along with any lifetime AAS use. One logistic regression model was estimated to determine the association between concurrent substance use and lifetime AAS use. RESULTS: Compared to no use, lifetime triple use (adjusted odds ratio 3.95, 95% confidence interval 1.73-8.95) was associated with lifetime AAS use while adjusting for potential confounders. CONCLUSIONS: Findings underscore an overlapping pattern of problematic substance use that may be harmful for adolescents. Health care professionals should be aware of these patterns to improve substance use assessment protocols.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".