ANTISOCIAL PERSONALITY DISORDER AND GAMBLING: COMMENTS ON PIETRZAK & PETRY (2005)
Bibliographic record
Abstract
I was pleased to read the article entitled ‘Antisocial personality disorder is associated with increased severity of gambling, medical, drug and psychiatric problems among treatment-seeking pathological gamblers’ by Robert H. Pietrzak & Nancy M. Petry in Addiction[1]. In the study, the authors used a relatively larger sample of treatment-seeking pathological gamblers not only to replicate prior published studies but also to explore other problems associated with pathological gambling such as medical and drug problems, etc. which are influenced by antisocial personality disorder (ASPD). However, the article left this reader with some questions. First, the prevalence of pathological gambling with ASPD reported by the authors (16.5%) is much lower than that observed in other community-based studies, including the St Louis ECA study (35%) [2] and the Edmonton Epidemiologic Catchment Area study in Canada (40%) [3]. The differences might be due to using different diagnostic criteria for ASPD [from Diagnostic and Statistical Manual version III (DSM-III) to DSM-IV], by increased accessibility to gambling or by different study parameters. Substance abuse and depression are common comorbidities for pathological gamblers. Many people with ASPD have problems of substance abuse or depression as well. It is not clear from the study's findings that the increased severity of pathological gambling was truly caused by ASPD or by substance abuse or depression. Secondly, all the participants were treatment seekers and screened first by telephone interview. Most probably, the findings are not generalizable to non-treatment-seeking pathological gamblers with or without ASPD due to sample selection bias. Also, as the sample was selected from Connecticut, the comparison of pathological gamblers with or without ASPD in other states or countries with varying access to gambling facilities may present different result patterns. Thirdly, the logistic regression analysis on the predictors of ASPD gave inconsistent results in terms of whether or not age and education are predictors for ASPD. The first step of analysis of demographic variables alone showed significant results on age and education as predictors for ASPD. But in the second step, which included gambling, drug and medical variables, the age and education were no longer independent predictors for ASPD in pathological gamblers. The authors did not make it clear whether or not age or education is a predictor of ASPD for pathological gamblers. Previous studies on a twin sample have shown that age and education are not predictors for ASPD [4]. In conclusion, the study employed a relatively larger sample and some of the results are consistent with previous research findings on pathological gambling with ASPD. However, the sample selection bias, and the much lower prevalence of ASPD in pathological gamblers compared with that from the general population-based studies, make the validity and generalizability of the study questionable. More research with randomized large samples is needed in order to extend this study to general populations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; 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".