Conduct Disorder-Related Hospitalization and Substance Use Disorders in American Teens
Bibliographic record
Abstract
OBJECTIVE: Our study aimed to compare the demographic characteristics of conduct disorder (CD) inpatients versus other psychiatric inpatients in children and adolescents, and assess the association between conduct disorder patients and the spectrum of substance use disorders (SUD). METHODS: We included 800,614 psychiatric adolescent (12-18 years) inpatients, and this included 8885 inpatients (1.1%) primarily for conduct disorder in the Nationwide Inpatient Sample (2010-2014). ICD-9 codes were used to detect SUD, and a logistic regression model was used to evaluate the odds ratio (OR) for SUD in conduct disorder inpatients. RESULTS: A higher proportion of conduct disorder inpatients were of 12-15 years of age (62.6%), male (64.4%), and White (45.7%). The lower median household income was correlated with a higher prevalence of conduct disorder (36.4%). Among SUD, cannabis use (23.7%) was most prevalent in conduct disorder inpatients followed by tobacco and alcohol use (10.1% each). Conduct disorder inpatients have 1.7-fold higher odds (95% confidence interval (CI) 1.52-1.82) for alcohol use and 1.4-fold higher odds (95% CI 1.31-1.49) for cannabis use compared to the non-conduct disorder inpatients. Cannabis use was seen significantly in adolescents (49.1%, 12-15 years), male (75.6%), and African Americans (45.6%). CONCLUSION: Conduct disorder inpatients have a higher risk of comorbid SUD compared to other psychiatric illnesses. The most common substance to be abused is cannabis followed by tobacco and alcohol. Varying pattern of substance use was seen by demographics and these predictors may help the clinicians for early diagnosis and treatment to improve overall health-related quality of life.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".