Occasional cannabis use is associated with higher premorbid functioning and IQ in youth at clinical high-risk (CHR) for psychosis: Parallel findings to psychosis cohorts
Bibliographic record
Abstract
BACKGROUND: Neurocognitive deficits have been widely reported in clinical high-risk for psychosis (CHR) populations. Additionally, rates of cannabis use are high among CHR youth and are associated with greater symptom severity. Cannabis use has been sometimes shown to be associated with better neurocognition in more progressed psychosis cohorts, therefore in this study we aimed to determine whether a similar pattern was present in CHR. METHODS: CHR participants ages 12-30 from the North American Prodromal Longitudinal Study (NAPLS-3) (N = 698) were grouped according to: "minimal to no cannabis use" (n = 406), "occasional use" (n = 127), or "frequent use" (n = 165). At baseline, cannabis use groups were compared on neurocognitive tests, clinical, and functional measures. Follow-up analyses were used to model relationships between cannabis use frequency, neurocognition, premorbid, and social functioning. RESULTS: Occasional cannabis users performed significantly better than other use-groups on measures of IQ, with similar trend-level patterns observed across neurocognitive domains. Occasional cannabis users demonstrated better social, global, and premorbid functioning compared to the other use-groups and less severe symptoms compared to the frequent use group. Follow-up structural equation modeling/path analyses found significant positive associations between premorbid functioning, social functioning, and IQ, which in turn was associated with occasional cannabis use frequency. DISCUSSION: Better premorbid functioning positively predicts both better social functioning and higher IQ which in turn is associated with a moderate cannabis use pattern in CHR, similar to reports in first-episode and chronic psychosis samples. Better premorbid functioning likely represents a protective factor in the CHR population and predicts a better functional outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".