Alexithymia in War Veterans with Post-traumatic Stress Disorder
Bibliographic record
Abstract
Introduction Alexithymia consider a cluster of cognitive and affective characteristics that include: inability of recognizing and describing emotions, difficulties in distinguishing feelings and physical sensations during emotional arousal, narrowed capacity for imagination and externally oriented cognitive style. Several studies links alexithymia with increased risk for physical and mental damage. Symptoms of alexithymia are documented in persons who develop PTSD in response to different types of traumatic events. Objectives To examine alexithymia in war veterans. Aims To determine whether alexithymia is significantly more present in war veterans with PTSD. Methods Cross-sectional study of 205 war veterans tested by Harvard Trauma Questionnaire and by Toronto Alexithymia Scale (TAS-20). Results Out of 205 war veterans 89 (43.4%) of them have alexithymia. Significantly more veterans with PTSD (78 or 75%) than without PTSD (11 or 10.9%) has alexithymia (Chi 2 = 88.955, P < 0.001) was found a statistically significant difference between the two groups in the total score of alexithymia ( t -test = −10.676, P < 0.001) statistically significant difference was found in all three domains of alexithymia. Conclusions Alexithymia is significantly often in war veterans with than without PTSD. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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) | 0.003 | 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".