A Retrospective Study of the Clinical Characteristics Associated with Alcohol and Cannabis use in Early Phase Psychosis
Bibliographic record
Abstract
OBJECTIVE: Alcohol and cannabis misuse are common in patients with early phase psychosis (EPP); however, research has tended to focus primarily on cannabis misuse and EPP outcomes, with a relative lack of data on alcohol misuse. This retrospective cross-sectional EPP study investigated the relationship between cannabis, alcohol, and cannabis combined with alcohol misuse, on age, gender, psychotic, depressive and anxiety symptom severity, and social/occupational functioning, at entry to service. METHODS: Two-hundred and sixty-four EPP patients were divided into 4 groups based on substance use measured by the Alcohol, Smoking and Substance Involvement Screening Test: (1) no to low-level cannabis and alcohol misuse (LU), (2) moderate to high alcohol misuse only (AU), (3) moderate to high cannabis misuse only (CU), and (4) moderate to high alcohol and cannabis misuse (AU + CU). RESULTS: We found significant between group differences in age (with the AU group being the oldest and AU + CU group the youngest) as well as gender (with the CU group having the highest percentage of men). There were also group differences in positive psychotic symptoms (lowest in AU group), trait anxiety (highest in AU + CU group), and social/occupational functioning (highest in AU group). Further regression analyses revealed a particularly strong relationship between AU + CU group and trait anxiety (3-fold increased odds of clinical trait anxiety for combined misuse of alcohol and cannabis compared to non/low users). CONCLUSIONS: This study demonstrates the unique demographic and clinical characteristics found in the EPP population at entry to care associated with alcohol and cannabis misuse both separately and in combination. This work highlights the importance of including the assessment of alcohol misuse in addition to cannabis misuse in future treatment guidelines and research.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".