Genetic counselling for the prevention of mental health consequences of cannabis use: A randomized controlled trial‐within‐cohort
Bibliographic record
Abstract
BACKGROUND: Cannabis use is a risk factor for severe mental illness. However, cannabis does not affect everyone equally. Genetic information may help identify individuals who are more vulnerable to the harmful effects of cannabis on mental health. A common genetic variant within the AKT1 gene selectively increases risk of psychosis, only among those who use cannabis. Therapeutically oriented genetic counselling may enable us to reduce cannabis exposure among genetically sensitive individuals. METHODS: Using a trial-within-cohort design, we aim to test if genetic counselling, including the option to receive AKT1 rs2494732 genotype, reduces cannabis use. To this end, we have designed a genetic counselling intervention: Interdisciplinary approach to Maximize Adolescent potential: Genetic counselling Intervention to reduce Negative Environmental effects (IMAGINE). RESULTS: IMAGINE will be implemented in a cohort of children and youth enriched for familial risk for major mood and psychotic disorders. Approximately 110 eligible individuals aged 12-21 years will be randomized in a 1:1 ratio to be offered a single genetic counselling session with a board-certified genetic counsellor, or not. Allocated youth will also be invited to attend a follow-up session approximately 1 month following the intervention. The primary outcome will be cannabis use (measured by self-report or urine screen) at subsequent annual assessments as part of the larger cohort study. Secondary outcomes include intervention acceptability and psychopathology. CONCLUSION: This study represents the first translational application of a gene-environment interaction to improve mental health and test an intervention with potential public health benefits. This study is registered with clinicaltrials.gov (NCT03601026).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".