Risky Decision-Making but Not Delay Discounting Improves during Inpatient Treatment of Polysubstance Dependent Alcoholics
Bibliographic record
Abstract
BACKGROUND: High levels of impulsivity, characteristics of addicted patients, are known to be important predictors of relapse. However, so far, little is known about the stability or variability of two main components of impulsivity (delay discounting and decision-making). The present study examined the changes in impulsivity during the first week of an abstinence based, behavioral orientated inpatient treatment program. METHOD: Thirty-seven polysubstance dependent alcoholics completed the Delay Discounting Task (DDT), and the Iowa Gambling Task (IGT) using the original version with decks A'B'C'D', and an alternative version with decks K'L'M'N', for measuring decision-making, after 2 and 6 weeks of active treatment. RESULTS: It was found that performances on the IGT changed during treatment while performances on the DDT did not (test-retest period: 4 weeks). CONCLUSION: The results provide preliminary evidence that improvements in decision-making might be related to treatment effects. All patients followed a highly structured cognitive-behavioral treatment program, which might have enhanced their executive functioning (coping skills training).
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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) | 0.000 | 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".