Video densitometric assessment of aortic regurgitation after transcatheter aortic valve implantation: results from the Brazilian TAVI registry
Bibliographic record
Abstract
AIMS: We sought to examine the feasibility and reproducibility of a new video densitometric (VD) quantification of aortic regurgitation (AR) on aortography, and its long-term clinical impact. METHODS AND RESULTS: Using dedicated video densitometry software, AR after TAVI was quantified, and inter- and intra-observer reproducibility was investigated in 182 aortograms of the Brazilian TAVI registry. The aortograms were analysed using two software algorithms: 1) the quantitative regurgitation analysis (qRA) index interrogating the entire left ventricle (LV), and 2) a new method with the left ventricle outflow tract (LVOT) as a region of interest (ROI) (LVOT-AR). LVOT-AR was feasible in 64.8% vs. 29.7% of aortograms, compared with qRA index. Using the LVOT-AR, inter-observer variability was low (mean difference±standard deviation [SD]: 0.01±0.05, p=0.53), and the two observers' measurements were highly correlated (r=0.95, p<0.001). Patients with LVOT-AR >0.17 had a significantly higher one-year all-cause mortality risk compared with patients with LVOT-AR ≤0.17 (37.1% vs. 11.2%, p=0.0008). CONCLUSIONS: This study proposes an alternative methodology for AR assessment after TAVI by using the LVOT method (LVOT-AR) of VD angiography. The assessment of LVOT-AR is feasible, reproducible and potentially predictive of one-year mortality.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".