Myocardial infarction-induced acute stress and post-traumatic stress symptoms: the moderating role of an alexithymia trait – difficulties identifying feelings
Bibliographic record
Abstract
BACKGROUND: It has been acknowledged that medical life-threatening experiences such as an acute myocardial infarction (MI) often lead to acute stress disorder symptoms (ASS), which in turn can result in the development of post-traumatic stress symptoms (PTSS). Previous studies have suggested an association between various traumatic experiences and alexithymia. The association of alexithymia with ASS and PTSS in patients with MI is elusive. OBJECTIVES: The aim of this study was to examine the association of alexithymia with MI-induced ASS and PTSS in patients at high risk of developing PTSD. METHOD: = 154) were examined twice, once within 48 hours, and then again three months after acute MI. All patients completed the self-rating Acute Stress Disorder Scale (ASDS) within 48 hours after the cardiac event. Three months after hospital discharge, all patients completed the Toronto Alexithymia Scale (TAS-20) and underwent the Clinician-Administered PTSD Scale (CAPS), a structured interview to assess the severity of PTSS. Descriptive statistics, correlations, multivariate linear regressions, and moderation analysis were conducted. RESULTS: = 0.04). The scores of TAS-20 subscales DDF and EOT as well as the TAS-20 total score had no influence on the relationship between ASS and PTSS (p > 0.05). CONCLUSION: In MI patients with high levels of DIF, ASS predicted the development of PTSS. If replicated, the finding may inform emotion-oriented interventions to investigate whether increasing the capacity to identify feelings following acute MI could be beneficial in preventing the development of PTSS.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".