Relationship of alexithymia and temperament and character dimensions with lifetime post‐traumatic stress disorder in male alcohol‐dependent inpatients
Bibliographic record
Abstract
AIMS: The purpose of the present study was to evaluate the prevalence of lifetime post-traumatic stress disorder (PTSD) in male alcohol-dependent inpatients and to investigate the relationship of PTSD with alexithymia and temperament and character dimensions. METHODS: Participants were 156 consecutively admitted male alcohol-dependent subjects. Patients were investigated using the Clinician-Administered PTSD Scale (CAPS), the Toronto Alexithymia Scale (TAS-20) and the Temperament and Character Inventory (TCI). RESULTS: Among alcohol-dependent inpatients 32.1% were considered as having lifetime PTSD. Mean scores of alexithymia, novelty seeking (NS), harm avoidance (HA) and self-transcendence (ST) were higher in the PTSD group, whereas age and self-directedness (S) were lower. Among age and other factors of TAS-20, 'difficulty in identifying feelings (DIF)' predicted PTSD in a logistic regression model. When age and personality dimensions of TCI were taken as independent variables, S predicted PTSD in the logistic regression model. Finally, among subscales of TCI, 'impulsiveness versus reflection' (NS2) and 'congruent second nature versus bad habits' (S5) predicted PTSD. CONCLUSIONS: Alexithymia and personality traits, particularly high DIF and S scores are related with lifetime PTSD diagnosis, even when controlling for age among alcohol-dependent inpatients. Causal relationships between alexithymia, personality dimensions and PTSD, and their implications on treatment are not clear and should be evaluated in longitudinal studies.
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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) | 0.002 | 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".