Rapid Deployment Valves Are Advantageous in the Redo Setting: A Single-Centre Retrospective Study
Bibliographic record
Abstract
Background: The spectrum on how to manage aortic valve disease continues to widen. The purpose of this study is to add further clarification to the role of rapid deployment valves (RDVs) by comparing their outcomes with traditional sutured valves (TSVs) in the reoperative aortic valve replacement (AVR) setting. Methods: This study was a retrospective review of all patients undergoing a second surgical reoperation for aortic valve disease. Patients were categorized into 2 groups: RDV and TSV. Cox proportional hazards regression models were used to determine the association between exposures of interest and the primary and secondary outcomes, after adjusting for all the baseline characteristics. The primary outcome was major adverse cardiovascular events (MACE) within 3 years, which was the composite of all-cause death, readmission for myocardial infarct, readmission for stroke, and readmission for heart failure. Results: A total of 307 patients made up the study population from 2010 to 2019. Of those, 254 patients received TSV, and 53 patients received RDV. RDV patients were significantly older than TSV patients by 10 years, on average. Shorter cardiopulmonary bypass (CPB) times were found with the RDV group. There was no significant difference in the primary outcome of MACE within 3 years. Conclusions: This single-centre large cohort study of patients with reoperative AVR found that RDVs facilitate smoother operations by saving 1 hour of cross-clamp time and CPB time. Furthermore, RDVs have comparable outcomes with TSVs, despite the significantly older patient population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".