Optimizing DSM-IV-TR Classification Accuracy: A Brief Biosocial Screen for Detecting Current Gambling Disorders among Gamblers in the General Household Population
Bibliographic record
Abstract
OBJECTIVE: To develop a pathological gambling (PG) screen for efficient application to the household population and for clinicians to use with treatment seekers. METHOD: We applied a series of multivariate discriminant functions to past-12-month Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR)-based, gambling-related problems; the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) measured and collected this data. The NESARC conducted computer-assisted personal interviews with 43 093 households and identified the largest sample of pathological gamblers drawn from the general household population. RESULTS: We created a 3-item, brief biosocial gambling screen (BBGS) with high sensitivity (Sensitivity = 0.96; 76 of 79 pathological gamblers correctly identified) and high specificity (Specificity = 0.99; 10 892 of 11 027 nonpathological gamblers correctly identified). CONCLUSIONS: Major US studies reveal extensive comorbidity of PG with other mental illnesses. The BBGS features psychometric advantages for health care providers that should encourage clinicians and epidemiologists to consider current PG along with other problems. The BBGS is practical for clinical application because it uses only 3 items and they are easy to ask, answer, and include in all modes of interviewing, including self-administered surveys. The BBGS has a strong theoretical foundation because it includes 1 item from each of the addiction syndrome 3 domains: neuroadaptation (for example, withdrawal); psychosocial characteristics (for example, lying); and adverse social consequences of gambling (for example, obtaining money from others).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".