Personality traits and craving in patients undergoing alcohol withdrawal treatment
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is a public health problem in France. Links between personality dimensions and AUD have already been established, but few studies focused on individuals seeking alcohol withdrawal. The main objective of the study was to describe the personality profile of patients seeking alcohol detoxification in a complex residential care unit. The secondary objective was to investigate the relationship between personality dimensions and alcohol craving. METHOD: The observational longitudinal exploratory study included 88 patients with AUD who were hospitalized during alcohol withdrawal treatment. Personality dimensions (125-item Temperament and Character Inventory, TCI), alexithymia (French scale for assessing alexithymia), craving (the Obsessive Compulsive Drinking Scale), anxiety (the Hamilton Anxiety Rating Scale), depressive symptoms (the Beck Depression Inventory) and cognitive dysfunction (the Montreal Cognitive Assessment) were assessed using the specified instruments at the time of withdrawal and 3 months later. Carbohydrate Deficient Transferrin and Brain-Derived Neurotrophic Factor (BDNF) levels were also measured at baseline and 3 months later. RESULTS: Among 76 patients assessed, 29 relapsed at 3 months. Relapsers showed higher novelty-seeking (mean ± SD = 63.2 ± 11.4 vs. 54.6 ± 10.8, p = 0.011), higher self-directedness (70.3 ± 9.1 vs. 63.0 ± 8.7, p = 0.009), and lower harm avoidance (48.7 ± 9.8 vs. 57.9 ± 10.5, p = 0.015). Logistic regression identified novelty-seeking as the strongest predictor of relapse (OR = 1.06, 95 % CI [1.01-1.11], p = 0.025). CONCLUSION: High novelty-seeking emerged as the main predictor of relapse three months after withdrawal. Although self-directedness appeared elevated in relapsers, this likely reflects transient self-regulation fostered by inpatient treatment rather than long-term resilience. Considering personality profiles in early recovery could help tailor relapse-prevention strategies in alcohol use disorder.
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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 | 0.001 | 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 | 0.001 |
| 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".