Alessitimia e personalità in pazienti con disturbi d'ansia e depressione maggiore: influenze sull'outcome di trattamento Alexithymia and personality in patients with anxiety disorders and major depression: effects on treatment outcome
Bibliographic record
Abstract
Summary Objectives Alexithymia is frequently associated with major depression and anxiety disorders, and in the literature it is considered as a negative prognostic factor in the treatment of these disorders. Nevertheless, limited evidence is available about the effect of alexithymia on treatment outcome. In this study, we analyze the presence of alexithymia in patients with depressive or anxiety disorders. In addition, we investigate the effects of alexithymia considered as a single factor, and the interaction between alexithymia and socio-demographics and clinical variables (personality traits) as modulators of response to combined treatment. Materials and methods Eighteen outpatients with major depression or anxiety disorders underwent pharmacotherapy and individual psychodynamic psychotherapy. Subjects were assessed using the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A) and the Toronto Alexithymia Scale 20-item (TAS-20) at baseline and after eight weeks of treatment. Personality traits were assessed with the SCID II at baseline. Outcome was the change in scores of HAM-D and HAM-A from baseline to the eight weeks of treatment. Predictor was the TAS score. The statistical relationship between outcome and predictor was analyzed by linear regression. In the regression model, we included stratification factors (socio-demographics and clinical variables) as covariates if they had a significant statistic relationship with the main outcome and their interaction with the main outcome is included. Results After eight weeks of treatment, we observed an improvement of 78% on HAM-D and of 69% on HAM-A, while 55.5% of subjects were not alexithymic, 22.2% were borderline alexithymic and 5.5% were diagnosed as alexithymic. No significant effect resulted from the analysis of alexithymia as a modulator of response to anxiety or depressive symptoms. On the other hand, the combination of alexithymia with “older age” predicted worse outcome by the HAM-D (p = 0.02873). Moreover, we observed a negative correlation between “obsessivecompulsive personality traits” and improvement on HAM-D (p = 0.002314), and a positive correlation between alexithymia and obsessive-compulsive personality traits (p = 0.02629).
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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 | 0.000 | 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) | 0.001 | 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".