Prediction of Optimal Deployment Projection for Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: Identifying the optimal fluoroscopic projection of the aortic valve is important for successful transcatheter aortic valve replacement (TAVR). Various imaging modalities, including multidetector computed tomography (MDCT), have been proposed for prediction of the optimal deployment projection. We evaluated a method that provides 3-dimensional angiographic reconstructions (3DA) of the aortic root for prediction of the optimal deployment angle and compared it with MDCT. METHODS AND RESULTS: Forty patients undergoing transfemoral TAVR at St Paul's Hospital, Vancouver, Canada, were evaluated. All underwent preimplant 3DA and 68% underwent preimplant MDCT. Three-dimensional angiographic reconstructions were generated from images of a C-arm rotational aortic root angiogram during breath-hold, rapid ventricular pacing, and injection of 32 mL contrast medium at 8 mL/s. Two independent operators prospectively predicted perpendicular valve projections. The implant angle was chosen at the discretion of the physician performing TAVR. The angles from 3DA, from MDCT, the implant angle, and the postdeployment perpendicular prosthesis view were compared. The shortest distance from the postdeployment perpendicular prosthesis projection to the regression line of predicted perpendicular projections was calculated. All but 1 patient had adequate image quality for reproducible angle predictions. There was a significant correlation between 3DA and MDCT for prediction of perpendicular valve projections (r=0.682, P<0.001). Deviation from the regression line of predicted angles to the postdeployment prosthesis view was 5.1±4.6° for 3DA and 7.9±4.9° for MDCT (P=0.01). CONCLUSIONS: Three-dimensional angiographic reconstructions and MDCT are safe, practical, and accurate imaging modalities for identifying the optimal perpendicular valve deployment projection during TAVR.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".