Multilevel Profiles of Neurobiological Profiles of Risk, and Resilience and Treatment Outcomes in Early-Stage Psychiatric Disorders: Associations With Longitudinal Functioning Trajectories-A Multi-Level Machine Learning Analysis
Bibliographic record
Abstract
Aims:The association between cannabis use and psychosis has emerged as a prominent societal and health service issue over the past decade.In this symposium, we provide a broad overview of the current state of the research into the prevalence and trends in cannabis related psychosis and also deal with potential mechanisms and new treatment approaches. Methods and Results:The first presentation provides an overview of the increasing prevalence of cannabis psychosis internationally and highlights that this is an issue of global concern.The second presentation explores the association between legalizing cannabis use and rates of adolescent psychosis in Canada.The third presentation presents data about the relationship between inflammatory markers and cannabis use in youth.The final presentation gives an overview of a new treatment clinic for people with psychosis and cannabis use disorder and gives the first outcome data from this innovative service.Our symposium will end with a discussant who has lived experience of a cannabis induced psychosis who will give his views of the research and thoughts on the future of this field.Conclusions: Taken together, these presentations provide an overview of trends and risk factors for cannabis psychosis alongside opportunities for intervention and support.Implications for policy and practice are discussed in partnership with an expert by experience.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".