Effects of the atypical stimulant modafinil on a brief gambling episode in pathological gamblers with high vs. low impulsivity
Bibliographic record
Abstract
Pathological gambling (PG) is a serious psychiatric disorder afflicting 1-3% of the general population. Experimental evidence indicates shared neurochemical substrates for PG and psychostimulant addiction. Impulsivity characterizes one key subtype of PG. Therefore, medications that ameliorate psychostimulant addiction and impulsive syndromes might also benefit impulsive PG subjects. The atypical stimulant, modafinil reduces cocaine abuse and impulsivity in patients with ADHD. The present study sought to determine if modafinil (200 mg) would reduce the reinforcing effects of slot machine gambling in PG subjects, and if this effect was stronger in high (H-I) vs. low (L-I) impulsivity subjects (N = 20). A placebo-controlled, double-blind, counterbalanced, repeated measures design was employed. Apart from bet size, which declined uniformly in both groups under drug, modafinil had bi-directional effects in the two groups. In H-I subjects, the drug decreased desire to gamble, salience of Gambling words, disinhibition and risky decision-making. In L-I subjects, modafinil increased scores on these indices. Modafinil also differentially affected blood pressure response to the game in the two groups. These findings for modafinil appear to fit well with a growing literature demonstrating bi-directional effects of D2 agonists as a function of trait impulsivity. Impulsivity could critically moderate medication response in PG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".