Ross procedure or mechanical aortic valve, which is the best lifetime option for an 18-year-old? A decision analysis
Bibliographic record
Abstract
Objectives: Identifying the optimal solution for young adults requiring aortic valve replacement (AVR) is challenging, given the variety of options and their lifetime complication risks, impacts on quality of life, and costs. Decision analytic techniques make comparisons incorporating these measures. We evaluated lifetime valve-related outcomes of mechanical aortic valve replacement (mAVR) versus the Ross procedure (Ross) using decision tree microsimulations modeling. Methods: Transition probabilities, utilities, and costs derived from published reports were entered into a Markov model decision tree to explore progression between health states for hypothetical 18-year-old patients. In total, 20,000 Monte Carlo microsimulations were performed to model mortality, quality-adjusted-life-years (QALYs), and health care costs. The incremental cost-effectiveness ratio (ICER) was calculated. Sensitivity analyses was performed to identify transition probabilities at which the preferred strategy switched from baseline. Results: From modeling, average 20-year mortality was 16.3% and 23.2% for Ross and mAVR, respectively. Average 20-year freedom from stroke and major bleeding was 98.6% and 94.6% for Ross, and 90.0% and 82.2% for mAVR, respectively. Average individual lifetime (60 postoperative years) utility (28.3 vs 23.5 QALYs) and cost ($54,233 vs $507,240) favored Ross over mAVR. The average ICER demonstrated that each QALY would cost $95,345 more for mAVR. Sensitivity analysis revealed late annual probabilities of autograft/left ventricular outflow tract disease and homograft/right ventricular outflow tract disease after Ross, and late death after mAVR, to be important ICER determinants. Conclusions: Our modeling suggests that Ross is preferred to mAVR, with superior freedom from valve-related morbidity and mortality, and improved cost-utility for young adults requiring aortic valve surgery.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".