Myocardial Infarction With No Obstructive Coronary Artery Disease and the 2023 Turkiye Earthquakes
Bibliographic record
Abstract
Background Increased rates of cardiac events at the time of natural disasters have been reported. However, the relationship between myocardial infarctions and earthquakes is less clear. We report on the rate of myocardial infarction with no obstructive coronary artery (MINOCA) disease during the 2023 Turkiye earthquakes. Methods All patients with a positive troponin undergoing a coronary angiogram at the Adana City Training and Research Hospital 2 months prior and 2 months subsequent to the February 6 th , 2023, Turkiye earthquakes were included. Patients with MINOCA were identified. Multivariate logistic regression analysis was performed to determine variables associated with a diagnosis of MINOCA. Results 619 patients underwent angiography during the study period—479 prior and 140 subsequent to the earthquake. The median age was 61 years and 73% male. MINOCA was diagnosed in 7.8% of the cohort. MINOCA was higher in the postearthquake period (pre: 3.8% vs. post: 21.4%; p < 0.001). The time period after the earthquake had the highest odds of a diagnosis of MINOCA (odds ratio: 5.76; 95% confidence interval: 2.90–11.44). Survival to hospital discharge was higher in the postearthquake period (pre: 89.4% vs. post: 97.9%; p < 0.001). Conclusion The rate of MINOCA increased after the Turkiye earthquakes on February 6 th , 2023. This knowledge provides new insight into the spectrum of myocardial infarction after natural disasters. Our work also highlights a potential survivorship bias, which may confound studies reporting on cardiac events after natural disasters. Future work to assess the potential long‐term adverse consequences of MINOCA in this population is suggested.
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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 | 0.001 | 0.028 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".