Examining the Association Between Psychiatric Disorders and Cocaine Binges: Results From the COSMO Study
Bibliographic record
Abstract
OBJECTIVES: Although cocaine binges and mental health problems have both been identified as significant risk factors for different health hazards, little is known about the relationship between mental health and cocaine binging. Hence, the aim of this study is to examine the association between psychiatric disorders and cocaine binge. METHODS: Participants were part of a prospective cohort study of individuals who either smoke or inject cocaine. The dependent variable, namely a cocaine binge within the past month, was defined as the repetitive use of large quantities of cocaine until the individual was unable to access more of the drug or was physically unable to keep using. Psychiatric disorders were assessed using the Composite International Diagnostic Interview and the Diagnostic Interview Schedule questionnaires. Logistic regression models were performed to examine the association between cocaine binging and psychiatric disorders, adjusting for potential confounders. RESULTS: Of the 492 participants, 24.4% reported at least 1 cocaine binging episode during the prior month. Among the study population, 48.0% met the criteria for antisocial personality disorder (ASPD), 45.5% for anxiety disorders, and 28.2% for mood disorders. Participants with ASPD were more likely to binge (adjusted odds ratio 1.73, 95% confidence interval 1.10-2.73), whereas those with a mood disorder were not. The association between anxiety disorders and cocaine binging was significant only in univariate analyses. CONCLUSION: ASPD increased the odds of reporting cocaine binge in our study population. These results highlight the need for a better understanding of the specific dimensions of ASPD that contribute to the increased risk of unsafe drug use behaviors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".