Self‐Reported Psychotic Disorders among Individuals with Substance Use Disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Comorbidity of substance use disorders (SUDs) and psychotic disorders (PDs) presents many challenges in diagnosis and treatment. Most reports to-date focus on the prevalence of SUDs among clinical populations of patients with PDs, and there is a lack of data pertaining to rates of PDs among individuals with substance use and SUDs. METHODS: We analyzed data on 43,093 respondents age 18 and above from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative US survey (Wave 1, 2001-2002). Cross-tabulations were used to derive prevalence estimates of PDs among individuals with 12-month substance use or SUDs across 10 categories of substances. Odds ratios (ORs) were derived from bivariate logistic regression analyses to examine the relationships between lifetime PDs and 12-month substance use or SUDs for the specific categories of substances. RESULTS: Among individuals with 12-month substance use, prevalence of PDs was found to be elevated in 8 of 10 categories of substances, particularly among amphetamine (OR = 8.8) and cocaine (OR = 10.3) users compared to nonusers. Among individuals with SUDs, prevalence of PDs was elevated in 9 of 10 categories of substances compared to individuals without SUDs. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Our findings on the increased rates of PDs among substance users and individuals with SUDs across a wide range of substances emphasize the importance of screening for PDs while treating patients with substance use and SUDs. This may allow for early intervention and adequate referral to appropriate settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it