Diagnostic Stability of First-Episode Psychotic Disorders and Persistence of Comorbid Psychiatric Disorders over 1 Year
Bibliographic record
Abstract
OBJECTIVE: Diagnostic stability is an important indicator of the reliability and validity of psychiatric diagnoses and has implications in clinical practice and research. While several studies have investigated the diagnostic stability of first-episode psychosis (FEP) disorders, less is known about psychiatric comorbidity in FEP and the persistence of such comorbid conditions over time. Our study aimed to confirm the diagnostic stability of FEP disorders and determine the variation in persistence of comorbid substance use disorders (SUDs), mood disorders, and anxiety disorders over 1 year. METHOD: The Structured Clinical Interview for DSM-IV-TR Axis I Disorders-Patient Edition was conducted at first presentation and repeated after 1 year (or reconstructed) for 214 FEP patients at the Prevention and Early Intervention Program for Psychoses-Montreal. RESULTS: Psychotic disorder diagnoses were retained by 76.2% of patients at 1 year, schizophrenia being the most stable diagnosis (92.1%). Most diagnostic shifts were to schizophrenia and schizophrenia spectrum disorders. Comorbid SUDs, anxiety disorders, and mood disorders persisted for 50.7%, 64.0%, and 16.7% of patients, respectively. Many new cases of each of these disorders also emerged at 1-year follow-up. CONCLUSIONS: These findings demonstrate the stability of primary psychotic disorder diagnoses and greater fluidity of comorbid psychiatric diagnoses, with anxiety disorders persisting as comorbid conditions more than mood disorders and SUDs. These results highlight the importance of repeating a structured diagnostic assessment longitudinally, especially for consideration of comorbid conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".