Modeling ability to resist alcohol in the human laboratory: A pilot study
Bibliographic record
Abstract
Background: Roughly half of patients with alcohol use disorder prefer non-abstinence based approaches to treatment. However, only individuals who can limit their alcohol use after low-risk consumption are most likely to benefit from these approaches. This pilot study developed a laboratory-based intravenous alcohol self-administration paradigm to determine the characteristics of individuals who could successfully resist consuming alcohol after an initial exposure. Methods: Seventeen non-treatment seeking heavy drinkers completed two versions of an intravenous alcohol self-administration paradigm designed to assess impaired control over alcohol use. In the paradigm, participants received a priming dose of alcohol and then entered a 120-min resist phase, in which they received monetary rewards if they resisted self-administering alcohol. We used Cox proportional hazards regression to determine the impact of craving and Impaired Control Scale scores on rate of lapse. Results: 64.7% of participants across both versions of the paradigm were unable to resist alcohol for the duration of the session. Craving at baseline (HR = 1.07, 95% CI 1.01-1.13, p = 0.02) and following priming (HR = 1.08, 95% CI 1.02-1.15, p = 0.01) were associated with rate of lapse. Individuals who lapsed endorsed greater attempts to control their drinking over the prior six months compared to individuals who resisted. Conclusions: This study provides preliminary evidence that craving may be predictive of risk of lapse in individuals who are trying to limit alcohol intake after consuming a small initial amount of alcohol. Future studies should test this paradigm in a larger and more diverse sample.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".