Impact of major earthquakes on the incidence of acute coronary syndromes – A systematic review of the literature
Bibliographic record
Abstract
Natural disasters such as tsunami, hurricanes, and earthquakes may have a negative impact on cardiac health. The aim of our systematic review is to evaluate the impact of earthquakes on the incidence of acute coronary syndromes and cardiac mortality and to examine the impact of the time of earthquakes on the incidence of acute coronary syndromes. MEDLINE and Cochrane databases were searched for studies assessing the impact of earthquakes on acute coronary syndromes from inception until December 20, 2017. Reference lists of all included studies and relevant review studies were also searched. A total of 26 studies on 12 earthquake disasters were included in the systematic review. The existing data show a significant negative impact of the Great East Japan, Christchurch, Niigata-Chuetsu, Northridge, Great Hanshin-Awaji, Sichuan, Athens, Armenia, and Noto Peninsula earthquakes on the incidence of acute coronary syndromes. By contrast, studies on the Newcastle, Loma Prieta, and Thessaloniki earthquakes did not show a significant correlation with myocardial infarction and cardiac mortality. In conclusion, earthquakes may be associated with increased incidence of acute coronary syndromes and cardiovascular mortality. There are conflicting data about the impact of the timing of earthquakes on the occurrence of acute coronary syndromes. Preventive measures to promote the adjustment of healthcare systems to treat cardiovascular diseases after natural disasters should be immediately implemented particularly in high-risk regions.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".