Specific impact of stimulant, alcohol and cannabis use disorders on first-episode psychosis: 2-year functional and symptomatic outcomes
Bibliographic record
Abstract
BACKGROUND: Many studies have concluded that cannabis use disorder (CUD) negatively influences outcomes in first-episode psychosis (FEP). However, few have taken into account the impact of concurrent misuse of other substances. METHODS: This 2-year, prospective, longitudinal study of FEP patients, aged between 18 and 30 years, admitted to early intervention programs in Montreal, Quebec, Canada, examined the specific influence of different substance use disorders (SUD) (alcohol, cannabis, cocaine, amphetamines) on service utilization, symptomatic and functional outcomes in FEP. RESULTS: Drugs and alcohol were associated with lower functioning, but drugs had a greater negative impact on most measures at 2-year follow-up. Half of CUD patients and more than 65% of cocaine or amphetamine abusers presented polysubstance use disorder (poly-SUD). The only group that deteriorated from years 1 to 2 (symptoms and functioning) were patients with persistent CUD alone. Outcome was worse in CUD than in the no-SUD group at 2 years. Cocaine, amphetamines and poly-SUD were associated with worse symptomatic and functional outcomes from the 1st year of treatment, persisting over time with higher service utilization (hospitalization). CONCLUSION: The negative impact attributed to CUD in previous studies could be partly attributed to methodological flaws, like including polysubstance abusers among cannabis misusers. However, our investigation confirmed the negative effect of CUD on outcome. Attention should be paid to persistent cannabis misusers, since their condition seems to worsen over time, and to cocaine and amphetamine misusers, in view of their poorer outcome early during follow-up and high service utilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".