High craving is associated with fewer abstinent days and lesser time to relapse during treatment in severe alcohol use disorder
Bibliographic record
Abstract
ABSTRACT Background: Craving, an integral aspect of addictive processes, underlies heavy alcohol consumption and alcohol use disorder (AUD). Western studies point out that craving is associated with relapse risks in AUD treatment. The feasibility of assessing and following up with craving dynamicity is not studied in the Indian context. Aim: We aimed to capture craving and explore its association with relapse in an outpatient facility. Methods: Among 264 treatment-seeking male participants (mean [SD] age = 36 [6.7] years) with severe AUD, craving was assessed according to the Penn Alcohol Craving Scale (PACS) at treatment initiation and two follow-up visits (median follow-up: 1, 2 weeks). Days to drink and percentage of days abstinent were acquired during the follow-ups (maximum follow-up days = 355). Those lost to follow-up were censored and considered as having relapsed. Results: High craving was associated with fewer days to drink when considered as a sole predictor ( P = 0.030). With covariates including medication at treatment initiation, high craving was marginally associated with fewer days to drink ( P = 0.057). Baseline craving was negatively associated with proximal percentage of days abstinent ( P = 0.015) and cravings at follow-ups negatively correlated with cross-sectional abstinent days (FU1: P = 0.009, FU2: P = 0.019). Craving reduced significantly over time ( P < 0.001), irrespective of the drinking status in follow-ups. Conclusion: Relapse is a real challenge in AUD. The utility of craving assessment in identifying relapse risk in an outpatient facility helps in identifying an at-risk population for future relapse. Thus better-targeted approaches in treating AUD can be developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".