GENETIC CONTRIBUTION TO CANNABIS USE AND OPIOID TREATMENT
Bibliographic record
Abstract
Background: Canada continues to face an opioid epidemic with 5,368 opioid apparent related deaths occurring between January and September of 2021. Methadone Maintenance Treatment (MMT), a form of Medication Assisted Treatment used to treat Opioid Use Disorder (OUD), has been reported to decrease opioid cravings and opioid use, however, individual differences exist in the effective dose of methadone. Further, individuals living with an OUD have higher rates of substance use including cannabis. A genetic component has been suggested to exist for both cannabis use and MMT outcomes, however inconsistent findings have been reported. Methods: Knowledge synthesis and primary genetic association studies were conducted. A protocol was prepared for the planning of a systematic review for Genome-Wide Association Studies (GWASs) of cannabis use. The full systematic review was then conducted, providing an assessment of the literature and a description of studies quality. A GWAS and Polygenic Risk Score (PRS) was then conducted for cannabis use and MMT outcomes, separately, in Europeans only. The top Single Nucleotide Polymorphisms (SNPs) were then analyzed separately by sex and sex interactions were conducted. Results: The systematic review included 6 studies, identifying 96 genetic variants associated with cannabis use. The GWASs for both cannabis use and MMT outcomes did not identify any significant results. A significant PRS was found for regular cannabis use and methadone dose. No sex-specific results were identified. Discussion: This thesis summarised the evidence on the genetics of cannabis use as well as employed GWASs and PRSs to investigate cannabis use and MMT outcomes within a European population. We were able to highlight gaps within the genetic literature of cannabis and MMT outcomes as well as identify areas of interest for future research.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".