Predicting risk factors for postoperative coronary artery bypass grafting using logistic regression and CHAID
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Non-fatal postoperative complications are postoperative morbidity that can affect the patient’s functional status and quality of life. Evaluation of postoperative morbidity is the step needed to assess and improve the quality of patient care. Therefore, a method is required in order to predict risk factors in evaluating a patient’s postoperative morbidity. After this the results will be used to determine the insurance premiums. In this research, the Logistic Regression are used to know the risk factors that would occur in patients who had undergone Coronary Artery Bypass Grafting (CABG) surgery. Then we use the CHAID method to classify readmission based on patient characteristics. Based on the two analyzes, it can be concluded that the CHAID analysis supports the Logistics analysis, there are two risk factors significantly influence the complications of patients after Coronary Artery Bypass Graft (CABG), namely Sex and Ejection Fraction.
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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 | 0.009 |
| 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 | 0.002 |
| 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 it