Peer Review of “Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis”
Bibliographic record
Abstract
This manuscript [1] presents an interesting study that explores temporal trends in various performance metrics for different types of prediction models used in the prediction of in-hospital mortality after cardiac surgery in the United Kingdom from 2012 to 2019.The data set was divided into 2 periods: from 2012 to 2016 for model training and internal validation and from 2017 to 2019 for external validation.The study evaluated 5 prediction models: logistic regression, support vector machine (SVM), random forest, extreme gradient boosting (XGBoost), neural network, and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II.The authors aimed to assess the model performance on 5 metrics (1 -expected calibration error [ECE], area under the curve [AUC], 1 -Brier score, F 1 -score, and net benefit) and proposed a composite metric, the clinical effectiveness metric (CEM), calculated as the geometric mean of the 5 mentioned metrics, as the primary metric.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".