The level of alexithymia in alcohol-dependent patients does not influence outcomes after inpatient treatment
Bibliographic record
Abstract
BACKGROUND: The inability of individuals with Alcohol Use Disorders (AUD) to recognize and describe their feelings and cravings may be due to alexithymia. Previous researches have shown evidence for a negative influence of alexithymia on treatment outcomes in patients with AUD. Therefore, it was hypothesized that high alexithymic patients with AUD would benefit less from cognitive behavioral therapy (CBT) compared with low alexithymic patients. METHODS: One hundred alcohol-dependent inpatients (DSM IV) were assessed with the Mini International Neuropsychiatric Interview for psychiatric disorders, the Toronto Alexithymia Scale (TAS-20), and the European Addiction Severity Index (EuropASI). Baseline alexithymia, as a categorical and continuous variable, was used to compare or relate baseline demographic and addiction characteristics, time in treatment, abstinence, and differences in addiction severity at 1-year follow-up. Analyses were performed using chi(2) test, analysis of variance or Kruskal-Wallis, paired t-tests or Wilcoxon's signed rank tests, multivariate logistic, and linear regression models, as appropriate. RESULTS: The prevalence of high alexithymia (TAS-20 > 61) was 45%. The total TAS-20 score correlated negatively with years of education (r = -.21; p = .04) and positively with the psychiatry domain of the EuropASI (r = .23; p = .04). Alexithymia showed no relation to abstinence, time in treatment, or change in severity of alcohol-related problems on the EuropASI. CONCLUSION: High alexithymic patients with AUD do benefit equally from inpatient CBT-like treatment as low alexithymic patients with AUD. SCIENTIFIC SIGNIFICANCE: Multimethod alexithymia assessments with an observer scale have been advised to judge the relationship with resulting outcome in CBT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".