Genetic aspects of pathological gambling: a complex disorder with shared genetic vulnerabilities
Bibliographic record
Abstract
AIMS: To summarize and discuss findings from genetic studies conducted on pathological gambling (PG). METHODS: Searches were conducted on PubMed and PsychInfo databases using the keywords: 'gambling and genes', 'gambling and family' and 'gambling and genetics', yielding 18 original research articles investigating the genetics of PG. RESULTS: Twin studies using the Vietnam Era Twin Registry have found that: (i) the heritability of PG is estimated to be 50-60%; (ii) PG and subclinical PG are a continuum of the same disorder; (iii) PG shares genetic vulnerability factors with antisocial behaviours, alcohol dependence and major depressive disorder; (iv) genetic factors underlie the association between exposure to traumatic life-events and PG. Molecular genetic investigations on PG are at an early stage and published studies have reported associations with genes involved in the brain's reward and impulse control systems. CONCLUSIONS: Despite the paucity of studies in this area, published studies have provided considerable evidence of the influence of genetic factors on PG and its complex interaction with other psychiatric disorders and environmental factors. The next step would be to investigate the association and interaction of these variables in larger molecular genetic studies with subphenotypes that underlie PG. Results from family and genetic investigations corroborate further the importance of understanding the biological underpinnings of PG in the development of more specific treatment and prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".