Six Months Post Myocardial Infarction Depression: Is Acute PTSD a Predisposing Condition?
Bibliographic record
Abstract
Background: Depression has been recognized has one of the most critical psychological issues following a Myocardial Infarction (MI), its presence associated with readmissions and death, augments healthcare costs and increases utilisation of services. For theoretical and clinical reasons, Post-traumatic Stress Disorder (PTSD) should be considered as a predisposing psychological condition for depression. However, its impact on depression’s intensity and presence 6 months after a MI has not been clearly assess. Methods: Out of the 870 eligible patients in three Canadian hospitals, 339 completed the research protocol. Patients completed a depression (BDI-II) and a PTSD (MPSS-SR) inventory 48 hours to 14 days post MI to assess the prevalence both disorders. They again completed the BDI-II six months after their MI to investigate the predisposing effect of PTSD on depression. Results: Based on the symptomatology cut-off point of their respective measurement instruments, the prevalence of comorbid PTSD-depression was 11.5%. Patients with PTSD symptomatology one month post-MI report high level of depression symptomatology 6 months after the MI. The level of depression at 6 months for comorbid patients was not different from the depressed or traumatized patients’ level at 1 month. Conclusion: The results suggest that the presence of PTSD symptomatology at 1 month is a predisposing condition for the development of depression and its evaluation in a post MI investigation routine is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".