Clozapine Treatment and Cannabis Use in Adolescents with Psychotic Disorders - A Retrospective Cohort Chart Review.
Bibliographic record
Abstract
OBJECTIVES: To examine the association between clozapine treatment and frequency of cannabis use in adolescents with co-occurring psychotic and cannabis use disorder in a retrospective cohort chart review. METHOD: We conducted a retrospective cohort chart review of patients diagnosed with a psychotic disorder and concurrent cannabis use disorder admitted to a tertiary care youth inpatient unit from 2010-2012. Longitudinal exposure and outcome data was coded month-by-month. Frequency of cannabis use was measured using a 7-point ordinal scale. Severity of psychosis was measured on a 3-point ordinal scale. Mixed effects regression modeling was used to describe the relationship between exposure and outcome variables. RESULTS: Thirteen patients had exposure to clozapine and fourteen had no exposure to clozapine. Cannabis use decreased in patients treated with clozapine, compared to patients treated with other antipsychotics (OR 2.8; 95% CI 0.97-7.9). Compared to no medication, clozapine exposure was associated with significantly less cannabis use (OR 7.1; 95% CI 2.3-22.3). Relative to treatment with other antipsychotics, clozapine exposure was significantly associated with lower severity of psychotic symptoms (OR 3.7; 95% CI 1.2-11.8). CONCLUSIONS: Clozapine may lead to decreased cannabis use and psychotic symptoms in adolescents with concurrent psychosis and substance use. Clinical trials are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".