Total Regurgitant Fraction to Predict Aortic Valve Surgery in Patients With Concomitant Aortic and Mitral Regurgitation
Bibliographic record
Abstract
Background: Concomitant aortic and mitral regurgitation (CAMR) is associated with poorer outcome compared with isolated aortic regurgitation (AR). Current prognostic assessment of AR does not include the magnitude of mitral regurgitation (MR). Cardiac magnetic resonance (CMR) can integrate volumetric data to obtain a novel combined parameter, total regurgitant fraction (TRF), which could have the potential ability to measure the combined effects of AR and MR on left ventricle (LV) overload. The aim of our study was to explore the usefulness of TRF in predicting the future need of aortic valve surgery in patients with CAMR. Methods and Results: Patients with CAMR and prior CMR studies were retrospectively recruited. A total of 45 patients were included, of whom 10 (22%) developed surgery indications. At the median follow-up time point (3.2 years), survival without surgery indication was 95% in the group with TRF < 40% compared to 90% in the group with aortic regurgitant fraction (ARF) < 29%. In contrast, 67% of patients with TRF ≥ 40% developed surgery indications after 3.2 years compared to 55% of patients with ARF ≥ 29%. In the multivariate analysis, the model including binary TRF had the highest hazard ratio of 13.846 (2.822 to 67.939, P = 0.001). Conclusions: TRF is a promising CMR parameter that could improve the prediction of the need for surgery in patients with CAMR. Further studies with larger populations should be performed to confirm these findings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".