Comparison of Methamphetamine Induced Psychosis and Primary Psychotic Disorder: Scoping Review of Social Cognition
Bibliographic record
Abstract
As many as 43% of methamphetamine users will have a psychotic episode in their lifetime. Of these, 30% will develop a primary psychotic disorder, such as schizophrenia. The current state of the literature does not currently allow us to determine who will develop a primary psychotic disorder following a methamphetamine-induced psychosis (MIPD). This distinction is important, since people with a first episode of a psychotic disorder need specific and rapid treatments to ensure optimal recovery. Social cognition could help predict distinct profiles. The aim of this scoping review is to evaluate the literature in order to extract the differences between MIPD and primary psychotic disorders in the domain of social cognition. Articles were recovered from PsychINFO, Medline and Web of science and were retained if they met the following inclusion criteria: (a) original research or meta-analyses, (b) complete or partial sample with a psychotic disorder diagnosis with comorbid methamphetamine use, or MIPD, (c) studies focusing on the difference between a methamphetamine-induced psychosis and a primary psychotic disorder, and (d) studies focusing on social cognition in psychotic or methamphetamine using population. A total of 17 articles were identified, with none directly aiming at distinguishing MIPD and primary psychotic disorder using social cognition. Future studies on social cognition are needed in order to determine differences in the severity of deficits between the two profiles.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".