Comorbidity of Axis I and II Mental Disorders with Schizophrenia and Psychotic Disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions
Bibliographic record
Abstract
OBJECTIVE: To examine the comorbidity of Axis I and II disorders within a community-based sample of adults with schizophrenia. METHODS: The study was conducted using data from the National Epidemiologic Survey of Alcohol and Related Conditions. A diagnosis of schizophrenia was based on respondents' self-report that they had been diagnosed by a health professional with schizophrenia or a psychotic illness or episode (SPIE). Axis I disorders and Axis II personality disorders (PDs) were assessed using the Alcohol Use Disorders and Associated Disabilities Interview Schedule. Mental and physical quality of life were assessed using the Medical Outcomes Study Short Form 12 questionnaire. RESULTS: The prevalence of SPIE was 0.9%. We used multiple logistic regression to examine the association between the presence and absence of SPIE in Axis I and II mental disorders. Each of the Axis I and II mental disorders examined were significantly associated with a diagnosis of SPIE after controlling for age, sex, education, marital status, and household income. CONCLUSIONS: Clinicians should be aware of the patterns and extent of psychiatric comorbidities that may exist in schizophrenia. Possible mechanisms of these associations are discussed.
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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 | 0.003 |
| 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 | 0.000 |
| 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".