The Ross procedure versus mechanical aortic valve replacement in young patients: a decision analysis
Bibliographic record
Abstract
OBJECTIVES: Our goal was to determine the range of perioperative mortality rates associated with the Ross procedure that results in a life expectancy similar to that seen with mechanical aortic valve replacement (mAVR) in young patients with aortic valve disease. METHODS: A fully probabilistic Markov microsimulation model with 1000 outer loops and 10 000 inner loops was constructed to compare gain in life expectancy and quality-adjusted life years between the index treatment with the Ross procedure versus mAVR for a theoretical cohort of young patients with aortic valve disease. Inputs for early deaths and late complications (death, stroke, bleeding, reoperation) were obtained from a single-centre study of 208 propensity score matched patients. In the primary analysis, the perioperative mortality rate for the Ross procedure was varied by increments of 0.5% to determine its impact on life expectancy and quality-adjusted life years. A 2-way sensitivity analysis was conducted to determine simultaneously the impact of the Ross reoperation rate and Ross reoperative mortality rate on life expectancy. RESULTS: Life expectancy was improved with the Ross procedure when the perioperative mortality rate with the Ross procedure was <2.5% and was equivalent to mAVR when the mortality rate was 2.5% to 5%. Similarly, when the perioperative mortality rate of the Ross procedure was between 4% and 5.5%, the quality-adjusted life years gained were similar between the Ross procedure and mAVR. Life expectancy was improved when the Ross procedure reoperative mortality rate was <7% at an incidence of Ross reoperations of 18% at 20 years. CONCLUSIONS: Improved life expectancy can be expected with the Ross procedure when the operative mortality rate is less than 2.5%.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".