Posttraumatic Stress Disorder and the Nature of Trauma in Patients with Cardiovascular Diseases: A Case-Control study
Bibliographic record
Abstract
Abstract Introduction A large body of evidence indicates a significant and morbid association between posttraumatic stress disorder (PTSD) and cardiovascular disease (CVD). Few studies, however, have addressed the range of trauma in this medical population, from massive heart attack, to defibrillator shock to previous interpersonal aggression. Objective The main objective of this study was to examine the nature of trauma associated with the development of PTSD in CVD patients. More precisely, we were interested in knowing if trauma was medical in nature and whether cumulative trauma resulted in PTSD. Methods We performed a 1:3 case-control study. The authors compared CVD patients diagnosed with PTSD (n=37) to those with adjustment disorder (n=111) in terms of trauma/stressor types and medical and demographic characteristics. Results Half (51%) of CVD patients suffering from PTSD had endured a medical trauma, 35% an external (non-medical) trauma, and 14% both. There were no significant differences with CVD patients diagnosed with adjustment disorder, 40% of them having experienced a medical stressor, 40% an external (non-medical) stressor and 20% both. Cumulative trauma was seen in only 19% of CVD patients suffering from PTSD. Traditional risk factors (female sex, younger age) were not prominent in CVD patients with PTSD as compared to those with adjustment disorder. Cases were, however, significantly more likely to have psychiatric antecedents and recent surgical interventions. Conclusions By uncovering characteristics of PTSD patients/trauma in CVD patients, this work will serve future research and clinical initiatives to better screen at-risk patients or at-risk medical situations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".