Predictors of Major Adverse Cardiovascular and Cerebrovascular Events After Acute Coronary Syndromes: A Retrospective Observational Study Using YoMDB Database
Bibliographic record
Abstract
Objectives: Despite the rapid aging of the population in Japan, clinical predictors for major adverse cerebrovascular and cardiovascular events (MACCE) in patients with new onset of acute coronary syndromes (ACS) have not been well studied. This study therefore aimed to identify the predictors of MACCE in the first onset of ACS patients requiring care. Materials and Methods: Using the Yokohama Original Medical Database, we identified 3,373 patients who experienced a first onset of ACS and had certified care information from April 2014 to March 2016. The incidence proportion of MACCE from June 2014 to March 2018 was retrospectively investigated. Each patient's independence of daily living (IDL) was classified as one of three categories (reference, mild and severe). Results: Predictors of MACCE were identified using multivariate logistic regression analysis. Impaired IDL was associated with increased MACCE, with adjusted odds ratios for reference, mild and severe of 1.00, 1.35 (95% confidence intervals 1.14-1.60) and 2.12 (95% confidence intervals 1.61-2.80; P for trend < 0.001), respectively. Conclusions: This study revealed that male sex, chronic kidney disease, atrial fibrillation, high-intensity statin use, low-intensity statin use, and lower IDL (representing less independence) were the predictors of MACCE requiring care for a first onset of ACS. Further research will be required to understand the results of interventions for the identified predictors of MACCE.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".