Which substances pose the greatest risk of substance use disorder after controlling for polysubstance use?
Bibliographic record
Abstract
Both physiological and epidemiological research suggest that certain psychoactive substances have a greater potential for abuse (e.g. heroin) than others (e.g. hallucinogens). The use of multiple substances is also associated with a higher risk of substance use disorder (SUD). The goal of the present study was to evaluate the association between the use of different substances and the risk of SUD while accounting for polysubstance use. Data from the 2021 National Survey on Drug Use and Health (n = 58,034, unweighted) were used. Eight different substances (i.e. Alcohol, Cannabis, Cocaine, Inhalants, Hallucinogens, Heroin, Methamphetamine, and Opiate Misuse) were compared with respect to their typical frequency of use; the prevalence of SUD among individuals using each substance; the odds of SUDs when controlling for polysubstance use; and the rate of other substance use among those with a substance-specific SUD. Notable differences were found regarding the frequency of use and the rate of SUD among individuals reporting past year use. Heroin and methamphetamine were associated with the highest risk of SUD across all analyses. In contrast, hallucinogens and inhalants were consistently identified as having the lowest risk. The present results confirm that certain substances appear to have an inherently greater association with SUD compared to other substances. While these findings are not fundamentally divergent from prior epidemiological studies or ranking systems, they provide a more solid empirical foundation for assumptions of differential risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".