Could cognitive deficits help distinguish methamphetamine-induced psychosis from a psychotic disorder with substance abuse?
Bibliographic record
Abstract
Dissociating a primary psychotic disorder (PPD) with concurrent substance use from substance-induced psychosis (SID) can be a difficult task. This study explored the possibility of distinguishing subgroups using cognitive functioning in order to potentially help diagnose individuals with a PPD co-occurring with substance-use and a methamphetamine (MA) induced psychosis. The hypothesis stipulates that individuals with a PPD should present with different cognitive deficits compared to individuals with SID. The study used the data collected as part of a longitudinal study (the MAPS project) that took place in Vancouver BC, Canada. One hundred and seventy-two individuals presenting with psychosis and MA abuse were recruited. Substance use, symptoms severity and cognitive deficits were assessed. A cluster analyses revealed two profiles: individuals in Cluster 1 had a poorer performance on the Gambling task net score (M = −28.1) and on the Hopkins Verbal Learning Test – Revised (HVLT-R; M = 63) % of retention score compared to those in Cluster 2. Individuals in Cluster 1 also had more negative symptoms, t = 2.29, p < 0.05 and were more likely to have had a psychiatric diagnosis, X 2(3) = 16.26, p < 0.001. Results suggest that cognitive predictors might help identify PPD that co-occur with MA abuse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".