Subtyping Study of a Pathological Gamblers Sample
Bibliographic record
Abstract
OBJECTIVE: To classify into subgroups a sample of pathological gambling (PG) patients according to personality variables and to describe the subgroups at a clinical level. METHOD: PG patients (n = 1171) were assessed with the South Oaks Gambling Screen; the Temperament and Character Inventory-Revised; the Symptom Checklist-90-Revised; Eysenck's Impulsivity Scales, a diagnostic questionnaire for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) PG criteria; and the Structured Clinical Interview for the DSM-IV, Axis I disorders, substance use module. Clinical measures were collected through a semi-structured interview. We performed a 2-step cluster analysis based on the above-mentioned personality variables. Clinical data were compared across clusters. RESULTS: Four clusters were generated. Type I (disorganized and emotionally unstable) showed schizotypic traits, high impulsiveness, substance and alcohol abuse, and early age of onset, as well as psychopathological disturbances. Type II (schizoid) showed high harm avoidance, social aloofness, and alcohol abuse. Type III (reward sensitive) showed high sensation seeking and impulsiveness but no psychopathological impairments. Type IV (high-functioning) showed a globally adaptive personality profile, low level of substance and alcohol abuse or smoking, and no psychopathological disturbances. CONCLUSIONS: At least 4 types of PG patients may be identified. Two types showed a response modulation deficit, but only one of them had severe psychopathological disturbances. Two other types showed no impulsiveness or sensation seeking and one of them even exhibited good general functioning. The different personality and clinical configuration of these clusters might be linked to different therapeutic approaches.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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 teacher head, 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".