Validation of Prosthetic Mitral Regurgitation Quantification Using Novel Angiographic Platform by Mock Circulation
Bibliographic record
Abstract
OBJECTIVES: This study aimed to validate a dedicated software for quantitative videodensitometric angiographic assessment of mitral regurgitation (QMR). BACKGROUND: Quantitative videodensitometric aortography of aortic regurgitation using the time-density principle is a well-documented technique, but the angiographic assessment of mitral regurgitation (MR) remains at best semi-quantitative and operator dependent. METHODS: Fourteen sheep underwent surgical mitral valve replacement using 2 different prostheses. Pre-sacrifice left ventriculograms were used to assess MR fraction (MRF) using QMR and MR volume (MRV). In an independent core lab, the CAAS QMR 0.1 was used for QMR analysis. In vitro MRF and MRV were assessed in a mock circulation at a comparable cardiac output to the in vivo one by thermodilution. The correlations and agreements of in vitro and in vivo MRF, MRV, and interobserver reproducibility for QMR analysis were assessed using the averaged cardiac cycles (CCs). RESULTS: In vivo derived MRF by QMR strongly correlated with in vitro derived MRF, regardless of the number of the CCs analyzed (best correlation: 3 CCs y = 0.446 + 0.994x; R = 0.784; p =0.002). The mean absolute difference between in vitro derived MRF and in vivo derived MRF from 3 CCs was 0.01 ± 4.2% on Bland-Altman analysis. In vitro MRV and in vivo MRV from 3 CCs were very strongly correlated (y = 0.196 + 1.255x; R = 0.839; p < 0.001). The mean absolute difference between in vitro MRV and in vivo MRV from 3 CCs was -1.4 ± 1.9 ml. There were very strong correlations of in vivo MRF between 2 independent analysts, regardless of the number of the CCs. CONCLUSIONS: In vivo MRF using the novel software is feasible, accurate, and highly reproducible. These promising results have led us to initiate the first human feasibility study comprising patients undergoing percutaneous mitral valve edge-to-edge repair.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".