Characteristics Associated With High-Intensity Binge Drinking in Alcohol Use Disorder
Bibliographic record
Abstract
High-intensity binge drinking, defined as consuming 2-3 times the level of a binge (4 or 5 drinks for women or men), increases the risks of overdose and alcohol-related cancer relative to lower levels of drinking. This study examined the relationship between high-intensity binge drinking and three domains hypothesized to contribute to alcohol use disorder (AUD): incentive salience, negative emotionality, and executive function. This cross-sectional study at the National Institute on Alcohol Abuse and Alcoholism examined 429 adults with AUD and 413 adults without a history of AUD. Drinking was assessed using the 90-day Timeline Followback interview. The AUD sample was divided into training and testing sets, and a machine learning model was generated in the training set and then applied to the testing set, to classify individuals based on if they had engaged in high-intensity binge drinking. We also conducted regression models for the following dependent variables: the presence of high-intensity binge drinking, frequency of high-intensity binge drinking, and number of drinks per of binge. Independent variables in these regression models were determined by variable selection from the machine learning algorithm and included time thinking about alcohol, depression rating, and positive urgency as representative variables for the three domains. These variables were assessed using self-report measures. The models were applied to the adults without a history of AUD to determine generalizability. The machine learning algorithm displayed reasonable accuracy when classifying individuals as high-intensity binge drinkers (area under ROC=0.74, 95% CI 0.67, 0.80). In adults with AUD, greater depression rating (OR=1.04, 95% CI 1.01, 1.070) and amount of time thinking about alcohol (OR=1.48, 95% CI 1.20, 1.91) were associated with greater likelihood of high-intensity binge drinking. They were also associated with greater frequency of high-intensity binge drinking days and greater number of drinks on binge occasions. Our findings suggest that incentive salience may contribute to high-intensity binge drinking in both controls and individuals with AUD. Negative emotionality was only associated with high-intensity binge drinking in individuals diagnosed with AUD, suggesting that it may be a consequence rather than a cause of high-intensity binge drinking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".