The incremental predictive value of frailty measures in elderly patients undergoing cardiac surgery: A systematic review
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
Emerging evidence demonstrates that frailty measures can predict adverse outcomes after cardiac procedures. Our objectives were to examine whether the inclusion of frailty measures adds incremental predictive value to existing surgical risk prediction models in patients undergoing cardiac surgery and to evaluate the reporting and methods of studies that investigated the prediction of frailty measures in cardiology. The inclusion of frailty measures adds incremental predictive value on existing perioperative risk-scoring systems. We systematically searched the EMBASE, MEDLINE, and Web of Science databases for relevant studies. Studies were included according to predefined inclusion criteria. The quality of included studies was appraised using the QUADAS-2 tool. Data were extracted and synthesized according to predefined methods. Twelve studies were included in the analysis. Included studies demonstrated the incremental predictive value of frailty measures on existing surgical risk models for mortality, but the predictive value of frailty measures alone was not consistent across literature. Few studies that investigated the predictive ability of frailty measures reported all important model performance measures. When comparing the predictive value of frailty measures with existing models, few studies reported if the frailty measurement was separately performed from the existing perioperative risk assessment. The addition of frailty measures to the existing perioperative risk models improved the prediction performance for mortality, but the incorporation of frailty assessment into perioperative risk assessment requires further evidence before making health policy recommendations.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it