P6493Depth of implantation for transcatheter aortic valves: do we understand what we are measuring?
Bibliographic record
Abstract
Background: Fluoroscopy, the mainstay for intraoperative imaging in patients undergoing transcatheter aortic valve replacement (TAVR), is a 2-dimensional imaging modality that is affected by parallax. This implies that the implantation depth appreciated on fluoroscopy can be inaccurate depending on viewing angle. The viewing angle providing the most accurate depth measurement shows the valve stent inflow plane and aortic valve annulus both in plane. This view may be different from the common projection view used for valve deployment with only the aortic valve in plane. Purpose: To explore how different viewing angles on fluoroscopy affect the depth of implantation measured for transcatheter aortic valves. Methods: A total of 20 patients (half with bicuspid aortic valve) who received self-expanding valves with post-TAVR multi-detector computed tomography were randomly selected for this retrospective analysis using the software package FluoroCT. After segmenting the native annulus and the prosthesis stent inflow plane, the viewing angle where their optimal projection curves intersect was recorded. Angles between the two planes and the minimal/maximal depth of implantation were measured in this view. The conventional depth of implantation was then measured by the distance between the non-coronary cusp and the lower edge of the stent at left anterior oblique/Cranial where cusps aligned.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".