The Connection between Alexithymia and Somatic Morbidity in a Population of Combat Veterans with Chronic PTSD
Bibliographic record
Abstract
PURPOSE: To investigate the connection between alexithymia and somatic illness, or, somatization, in veterans suffering from chronic combat-related post-traumatic stress disorder, PTSD. METHODS: Croatian combat veterans (N=127) were studied at the Department of Psychology, Zagreb Clinical Hospital Center. The diagnosis of PTSD was confirmed and verified according to the International Classification of Diseases (ICD-10). A version of the Mississippi Scale for Combat Related PTSD (M-PTSD) standardized for the Croatian population was used to assess the severity of PTSD. In addition to the clinical interview, the existence of alexithymia was confirmed by the score on the Toronto Alexithymia Scale (TA S-20). RESULTS: A statistically significant association was found between the total number of diagnosed physical illnesses and the scores on three subscales of an alexithymia questionnaire, the TA S-20, with a 1% risk (p<0.01, 0.487; 0.450; 0.335). Regression analysis confirmed the most statistically significant predictive value of the first item of the TA S-20, which refers to difficulty in identifying feelings (=0.408, p=0.019). The total score on the M-PTSD scale correlated significantly to the subscales for alexithymia. There was a statistically significant negative correlation of the total score on the M-PTSD scale with social support. CONCLUSION: The total scores obtained in this study, particularly those related to alexithymia, indicate the importance of this construct in the etiopathogenesis of somatic morbidity in the study population and confirm that as in other countries the TA S-20 is a useful instrument in Croatia for the assessment of this phenomenon.
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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 | 0.000 |
| 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".