Abstract 13264: Gender-Based Risk Prediction for 1-year Readmission Among Younger Women Hospitalized for Acute Myocardial Infarction
Bibliographic record
Abstract
Background: Younger women (≤55 years) are at higher risk for readmission within one-year of hospitalization following acute myocardial infarction (AMI) compared to similarly aged men, yet there are no gender-specific risk prediction models for this population. Prior research suggests that gender-related factors more comprehensively explain sex-based differences in readmission risk. The objective was to develop and validate a risk prediction model of 1-year post-AMI readmission in young women considering demographic, clinical, and gender-related factors. Methods: We used data from women enrolled in the VIRGO study (n =2,007), a prospective observational study of young patients aged ≤55 years hospitalized with AMI in the US. Data were obtained from patient interviews, medical record abstraction, and adjudicated hospitalization records. Bayesian Model Averaging was used for model selection in a derivation cohort of 1338 women and subsequently validated in the remaining 669 women. Results: Within 1-year post-AMI, 684 (34.1%) women were readmitted at least once with a majority of readmissions due to cardiac causes (57.5%). The final model contained 9 predictors: experiencing any in-hospital complications, physical health at baseline (SF-12), disease-specific quality of life (Seattle Angina Questionnaire), diabetes, history of congestive heart failure, low income (≤30,000 USD), depression, length of hospital stay, and employment status (Figure) . Of the 9 predictors, 5 were gender-related. The model was well calibrated (calibration plots) and exhibited modest discrimination (C statistic=0.66 in development and validation cohorts). Conclusions: Younger women with diabetes, depression, history of congestive heart failure, and longer hospital stays were more likely to be readmitted. While clinical factors were the strongest predictors of readmission within 1-year among younger women with AMI, gender-related variables were important complements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".