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Record W1817556688 · doi:10.1111/add.12407

Environmental factors selectively impact co‐occurrence of problem/pathological gambling with specific drug‐use disorders in male twins

2013· article· en· W1817556688 on OpenAlexaff
Hong Xian, Justine L. Giddens, Jeffrey F. Scherrer, Seth A. Eisen, Marc N. Potenza

Bibliographic record

VenueAddiction · 2013
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsCannabisStimulantPathologicalCannabis DependencePsychologySubstance abusePsychiatryAlcohol dependenceNicotineTwin studyClinical psychologyMedicineInternal medicineHeritabilityChemistryGeneticsAlcohol

Abstract

fetched live from OpenAlex

AIMS: Multiple forms of drug abuse/dependence frequently co-occur with problem/pathological gambling (PPG). The current study examines the extent to which genetic and environmental factors contribute to their co-occurrence. DESIGN: Bivariate models investigated the magnitude and correlation of genetic and environmental contributions to problem/pathological gambling and its co-occurrence with nicotine dependence, cannabis abuse/dependence and stimulant abuse/dependence. SETTING: Computer-assisted telephone interviews in the community. PARTICIPANTS: Participants were 7869 male twins in the Vietnam Era Twin Registry, a USA-based national twin registry. MEASUREMENTS: Life-time DSM-III-R diagnoses for problem/pathological gambling, nicotine dependence, cannabis abuse/dependence and stimulant abuse/dependence were determined using the Diagnostic Interview Schedule. FINDINGS: All drug-use disorders displayed additive genetic and non-shared environmental contributions, with cannabis abuse/dependence also displaying shared environmental contributions. Both genetic [genetic correlation rA = 0.22; 95% confidence interval (CI) = 0.10-0.34] and non-shared environmental components (environmental correlation rE = 0.24; 95% CI = 0.10-0.37) contributed to the co-occurrence of problem/pathological gambling and nicotine dependence. This pattern was shared by cannabis abuse/dependence (rA = 0.32; 95% CI = 0.05-1.0; rE = 0.36; 95% CI = 0.16-0.55) but not stimulant abuse/dependence (SAD), which showed only genetic contributions to the co-occurrence with problem/pathological gambling (rA = 0.58; 95% CI = 0.45-0.73). CONCLUSIONS: Strong links between gambling and stimulant-use disorders may relate to the neurochemical properties of stimulants or the illicit nature of using 'hard' drugs such as cocaine. The greater contribution of environmental factors to the co-occurrence between problem/pathological gambling and 'softer' forms of drug abuse/dependence (cannabis, tobacco) suggest that environmental interventions (perhaps relating to availability and legality) may help to diminish the relationship between problem/pathological gambling and tobacco- and cannabis-use disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.327
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations88
Published2013
Admission routes1
Has abstractyes

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