Acute Stress Disorder After Myocardial Infarction: Prevalence and Associated Factors
Bibliographic record
Abstract
Objective: To examine the prevalence of acute stress disorder (ASD) after a myocardial infarction (MI) and the factors associated with its development. Methods: Of 1344 MI patients admitted to three Canadian hospitals, 474 patients did not meet the inclusion criteria and 393 declined participation in the study; 477 patients consented to participate in the study. A structured interview and questionnaires were administered to patients 48 hours to 14 days post MI (mean ± standard deviation = 4 ± 2.73 days). Results: Four percent were classified as having ASD using the Structured Clinical Interview for DSM-IV, ASD module. The presence of symptoms of depression (Beck Depression Inventory; odds ratio (OR) = 29.92) and the presence of perceived distress during the MI (measured using the question “How difficult/upsetting was the experience of your MI?”; OR = 3.42, R2 = .35) were associated with the presence of symptoms of ASD on the Modified PTSD Symptom Scale. The intensity of the symptoms of depression was associated with the intensity of ASD symptoms (R = .65). The models for the detection and estimation of ASD symptoms were validated by applying the regression equations to 72 participants not included in the initial regressions. The results obtained in the validation sample did not differ from those obtained in the initial sample. Conclusions: The symptoms of depression and the subjective distress during the MI could be used to improve the detection of ASD. ASD = acute stress disorder; PTSD = posttraumatic stress disorder; MI = myocardial infarction; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision; SCID-IV-ASD = Structured Clinical Interview for DSM-IV; MPSS-SR = Modified PTSD Symptom Scale—Self-Report; BDI-II = Beck Depression Inventory, Second Edition; LESS = Life Events Stress Scale; M-MSSS = Modified Medical Outcomes Study Social Support Survey; BMI = body mass index.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".