Machine Learning as a New Frontier in Mitral Valve Surgical Strategy
Bibliographic record
Abstract
One of the surgical options available for ischemic mitral regurgitation is mitral valve repair but is limited by recurrent regurgitation as it is experienced by a significant percent of patients and has a negative impact on patient outcomes. Efforts to model and identify predictors of recurrent MR rely on complicated echocardiographic and clinical measurements that are subjective and not routinely collected. Kachroo et. al. approached this problem in a unique way by using the STS database and Machine Learning to develop models that predict recurrent MR or death at one year. The STS database contains many routinely collected demographic and clinical parameters but requires a methodology, such as Machine Learning, that will accommodate collinearity and the unknown significance of many predictors. Kachroo et. al. developed three good Machine Learning models with AUC 0.72-0.75. Data- driven selection of important predictors showed that three revascularization targets, peripheral vascular disease and use of beta blockers are most predictive of recurrent mitral regurgitation. We applaud the authors in pioneering a novel methodology and paving the way for a bright future in Machine Learning which includes integrating medical imaging, waveform, and genomic data to practice personalized medicine for our patients.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".