Diagnostic validity of alcohol demand and monetary delay discounting in relation to alcohol use disorder
Bibliographic record
Abstract
BACKGROUND: A reinforcer pathology approach to alcohol use disorder (AUD) proposes that high alcohol reinforcing value (high alcohol demand) and overvaluation of immediate rewards (high discounting of future rewards) are critical determinants of problematic drinking. Applied to clinical settings, these indicators have not been examined as potential assessments for use in clinical practice. Toward that end, the current study examined whether reinforcer pathology indicators accurately classify individuals with AUD from recreational drinkers without AUD at levels that would meet clinical accuracy benchmarks. METHODS: In a case-control sample of 267 Canadian adults (180 meeting DSM-5 criteria for AUD), receiver operating characteristic (ROC) curves were constructed using reinforcer pathology indicators from an alcohol purchase task and a monetary delay discounting tasks. RESULTS: Analysis of the ROC curves revealed that three alcohol demand indices from the alcohol purchase task [i.e. Intensity (observed consumption when free), Omax (observed maximum expenditure), and α (a derived index of price-sensitivity)] significantly differed by groups and met established clinical benchmarks for diagnostic differentiation. While delay discounting significantly differed between groups, it did not meet benchmarks as a clinical differentiator. CONCLUSIONS: This study provides evidence that behavioral economic measures of alcohol valuation accurately diagnostically discriminate individuals with AUD from recreational drinkers. Future work should consider utilizing other behavioral economic indices and validating these results in more diverse populations.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".