Meta-analysis of risk factors for posttraumatic stress disorder in myocardial infarction
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to identify the risk factors for posttraumatic stress disorder in patients with myocardial infarction. METHODS: Cohort, case-control, and cross-sectional studies on posttraumatic stress disorder (PTSD) in patients with myocardial infarction were searched from PubMed, Embase, Cochrane Library, Web of Science, China Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Technology Journal database. The Newcastle-Ottawa Quality Assessment Scale was used to score the quality of the included literature in the cohort and case-control studies, and the cross-sectional studies were scored using the American Agency for Health Care Quality and Research cross-sectional study quality evaluation criteria. The literature was screened independently by 2 researchers, and if there was no consensus, the inclusion was decided by a third party. The extraction content included first author, publication year, sample size, PTSD assessment tool, PTSD assessment time, PTSD incidence, influencing factors, and study type. Meta-analysis of data was performed using Stata17.0 software. RESULTS: Ten studies were included, including 2 cohort studies, 7 cross-sectional studies, and 1 case-control study, with a total sample size of 2371 patients, including 26 influencing factors. The results of meta-analysis showed that the prevalence of PTSD in patients with myocardial infarction was 21.2%. Statistically significant influencing factors were gender (odd ratio [OR] = 3.124), neuroticism score (OR = 2.069), and age (OR = 0.913). CONCLUSIONS: The prevalence of PTSD in patients with myocardial infarction in China is higher than that in other countries. Female and neurotic personality are risk factors for developing PTSD in patients with myocardial infarction, and old age is protective factor for developing PTSD in patients with myocardial infarction. Targeted measures should be taken to prevent and reduce the occurrence and development of PTSD in patients with myocardial infarction in the future.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".