Predictors of Persistent Psychotic Symptoms in Persons With Methamphetamine Abuse Receiving Psychiatric Treatment
Bibliographic record
Abstract
The objective of this study was to identify predictors of sustained psychotic symptoms after methamphetamine (MA) abuse during the course of 6 months from patterns of MA and other substance use, depressive symptoms, family history of psychosis, antisocial personality disorder, and trauma history. A total of 295 individuals with MA abuse and psychotic symptoms seeking psychiatric services were assessed at baseline and then monthly on symptoms and substance use for 6 months. Trajectory analyses revealed two trajectories of the individuals with positive symptoms, with one group presenting with persistent psychotic symptoms (30% of the sample). Those with persistent psychosis were significantly older, had more severe psychotic symptoms, misused MA for more years, had more antisocial personality traits, and had more sustained depressive symptoms. The strongest predictors of belonging to the persistent psychosis group, via logistic regressions, were more severe psychotic symptoms, longer use of MA, and sustained depressive symptoms. Our results highlight the important comorbidities, especially regarding depressive symptoms and persistent psychosis, in individuals seeking psychiatric help after MA abuse. This study also highlights the importance of identifying people with persistent psychosis within MA users to facilitate rapid and effective treatment of co-occurring psychotic disorder.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".