Getting lucky: Clinical characterization of compulsive sexual behavior compared with gambling disorder and intermittent explosive disorder
Bibliographic record
Abstract
This study provides a comparative description of treatment-seeking adults with CSBD, Gambling Disorder (GD), and Intermittent Explosive Disorder (IED) across demographic, psychiatric, and personality correlates. Participants (N = 969; CSBD n = 233, GD n = 634, IED n = 102) were evaluated at a single specialty center. CSBD was ascertained using Goodman's criteria; GD and IED were diagnosed with DSM-IV SCID procedures. Psychiatric comorbidities were assessed with the Mini-International Neuropsychiatric Interview v5.0.0, and personality with the Temperament and Character Inventory. Group differences were tested with chi-square/Fisher's exact tests and Kruskal-Wallis with Dunn's post hoc tests using familywise Bonferroni thresholds, followed by backward stepwise binary logistic regressions contrasting CSBD vs. GD and CSBD vs. IED. Relative to GD, the CSBD group was younger, more often male, more frequently non-heterosexual, and reported higher educational attainment. CSBD also showed lower odds of hypomania and anorexia nervosa (binge/purge subtype) and lower novelty-seeking, with trait means in the normative range. Relative to IED, CSBD was more often non-heterosexual, less likely to have bulimia nervosa, and showed higher cooperativeness and self-transcendence, with trait means in the normative range. Findings delineate phenotypic patterns in a treatment-seeking cohort that can sharpen screening, triage, and case formulation. Results should be interpreted as clinical characterization rather than evidence for nosological proximity, given the cross-sectional design and historical diagnostic frameworks used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".