A neuropsychological approach to differentiating <scp>cannabis‐induced</scp> and primary psychotic disorders
Bibliographic record
Abstract
AIM: Rates of cannabis use are elevated in early psychosis populations, rendering it difficult to determine if an episode of psychosis is related to cannabis use (e.g., cannabis-induced psychosis), or if substance use is co-occurring with a primary psychotic disorder (e.g., schizophrenia). Clinical presentations of these disorders are often indistinguishable, hindering assessment and treatment. Despite substantial research identifying cognitive deficits, eye movement abnormalities and speech impairment associated with primary psychotic disorders, these neuropsychological features have not been explored as targets for diagnostic differentiation in early psychosis. METHODS: = 7.65, 17 male) were recruited from early intervention programs. Diagnoses were ascertained by primary treatment teams after a minimum of 6 months in the program. Participants completed tasks assessing cognitive performance, saccadic eye movements and speech. Clinical symptoms, trauma, substance use, premorbid functioning and illness insight were also assessed. RESULTS: Relative to individuals with primary psychosis, individuals with cannabis-induced psychosis demonstrated significantly better performance on the pro-saccade task, faster RT on pro- and anti-saccade tasks, better premorbid adjustment, and a higher degree of insight into their illness. There were no significant differences between groups on psychiatric symptoms, premorbid intellectual functioning, or problems related to cannabis use. CONCLUSIONS: In early stages of illness, reliance on traditional diagnostic tools or clinical interviews may be insufficient to distinguish between cannabis-induced and primary psychosis. Future research should continue to explore neuropsychological differences between these diagnoses to improve diagnostic accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".