Cannabis & Psychosis: The Interface Emerging Frontiers for Research
Bibliographic record
Abstract
INTRODUCTION: CANNABIS CONTINUES TO AFFECT MENTAL HEALTH. ITS ABUSE IS ON RISE GLOBALLY. IN CANADA A RISE BY 30% IN LAST TEN YEARS HAS BEEN OBSERVED IN HIGH SCHOOL STUDENTS. INTERRELATIONSHIP OF CANNABIS WITH PSYCHOSIS AND SCHIZOPHRENIA IS A COMPLEX ONE. CANNABIS IS HIGHLY COMORBID WITH PSYCHOSIS, & RELATED TO FUNCTIONAL DISABILITY AND OUTCOME. IT POSES SEVERAL CHALLENGES IN UNDERSTANDING CAUSAL RELATIONSHIP FOR COMORBIDITY, UNDERLYING NEUROCHEMICAL BASIS AND SPECIFICS OF SERVICE DEVELOPMENT. PREVALENCE OF CANNABIS VARIES FROM 20 TO 50% EARLY PSYCHOSIS. OBJECTIVE OF THIS PAPER IS TO REVIEW AVAILABLE LITERATURE TO IDENTIFY CHALLENGES FOR NEWER TARGETS OF RESEARCH AND PREVENTIVE MEASURES.\nMETHOD: RECENT LITERATURE FROM ELECTRONIC DATA BASE SEARCH IDENTIFIES ROLE AND RELATIONSHIP OF CANNABIS AND PSYCHOSIS.\nRESULTS. CANNABIS IS A RISK FACTOR FOR BOTH PSYCHOSIS AND SCHIZOPHRENIA & APPEARS TO HAVE CAUSAL RELATIONSHIP FOR EARLY AND LATERAGE PSYCHOSIS. MOOD SYMPTOMS ARE ALSO SIGNIFICANT BUT LESS RECOGNIZED. UNDERSTANDINGS OF THE PROCESS AND CAUSES HAVE SIGNIFICANTLY ADVANCED WITH DISCOVERY OF CANNABINOID RECEPTORS AND ENDOGENOUS CANNABINOIDS. IT IS CLEAR THAT CANNABIS INCREASES BRAIN VULNERABILITY, CAUSES POORER OUTCOME AND MORE SIDE EFFECTS. CANNABIS CAUSES COGNITIVE DYSFUNCTION THAT PERHAPS WORKS AS A COMMON DENOMINATOR FOR THE RISK-VULNERABILITY. IT APPEARS TO HAVE INDEPENDENT GENETIC COMPONENT RELATED TO DISRUPTION IN NEUROTRANSMISSION AFFECTING NEURONAL PLASTICITY. MUCH LESS ATTENTION HAS BEEN PAID IN DEVELOPING SERVICES TARGETED TOWARDS HARM REDUCTION AND DEVELOPING THERAPEUTICS.\nCONCLUSION. CANNABIS IS POTENTIAL RISK FACTOR FOR POORER OUTCOME IN PSYCHOSIS. NEW BIOLOGICAL AND SOCIAL SERVICE INITIATIVES WILL ADD VALUE TO EARLY PSYCHOSIS PROGRAMS.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".