Decision-Making Measured by the Iowa Gambling Task in Patients with Alcohol Use Disorders Choosing Harm Reduction versus Relapse Prevention Program
Bibliographic record
Abstract
AIMS: Two main therapeutic programs were offered to patients suffering from alcohol use disorders (AUDs): avoid the alcohol by abstinence or controlling their consumption. After information and motivational sessions, the patient chooses his own therapeutic plan. However, patients with AUD exhibit poor decision-making. The purpose of this study was to investigate the decision-making in AUD by comparing patients who chose to reduce and control their consumption to those who chose abstinence program. METHODS: Sixty-seven subjects with alcohol use disorder were included (AUD group) for treatment, choosing either a relapse prevention program (RPP) or a harm reduction program (HRP). Patients were compared to a healthy control group (n = 31). Cognitive skills were assessed through the Montreal Cognitive Assessment test, the National Adult Reading Test, the Trail Making Test and the Iowa Gambling Task (IGT). RESULTS: Thirty-seven patients with AUD chose the RPP while 30 followed a HRP. The AUD group performed worse than controls on the IGT. The RPP group had significantly lower performance than both HRP and control groups (these later groups being not statistically different). No correlation was observed between the available clinical, cognitive and intellectual measures. CONCLUSION: This study confirms that the decision-making process of patients with an alcohol use disorder is impaired. However, the 2 groups differ on the IGT scores, despite comparable clinical and cognitive profiles. The patients' decision-making abilities could be a useful guide when developing therapeutic programs.
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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 | 0.000 | 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 | 0.000 |
| 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".