Abstract 12227: New Insights Into Calcification and Aortic Stenosis Using 4-dimensional Computed Tomography.
Bibliographic record
Abstract
Background: Aortic Valve Calcification (AVC) is intrinsic to aortic stenosis (AS). While established concepts assume homogeneous calcification causing AS, wide variability in hemodynamic severity at any given AVC burden suggests other operative mechanisms. Multidetector computed tomography (MDCT) now accurately measures global AVC, but AVC spatial distribution remains elusive due to imaging orientation. Methods and Results: We developed ‘en-face’ imaging by re-registration of 4D-MDCT to quantify AVC spatial distribution and analyzed global AVC load and AVC distribution in 418 patients with AS (76±9 years; mean gradient 35±17 mmHg). 4D-volume-rendered MDCT datasets were re-oriented to en-face view of aortic valve for AVC spatial scoring with individual cusp calcification load, cusp-edge calcification and AVC asymmetry. Despite high total AVC load (450 [250-666] AU/cm 2 ), asymmetry was frequent (50%), with a difference between most- and least-calcified cusp of 112 [66-182] AU/cm 2 . Maximum AVC was in the non-coronary cusp in 61% (p<0.001). Cusp edge calcification was none-mild in 26%, moderate in 62% and severe in 12%. Adjusting for total AVC, severe AS (mean gradient >40 mmHg) was more likely with symmetrical AVC (odds ratio [OR] 2.36, p<0.001) and with edge calcification moderate (OR 4.16, p=0.001 vs none-mild) to severe (OR 10.7, p=0.001). Inclusion of AVC distribution improved models predicting AS severity over total AVC (p<0.001). Conclusions: Four-dimensional MDCT en-face re-registration and AVC quantitation provides new insight into AS pathophysiology. Contrary to classical concepts, AVC is frequently inhomogeneous and asymmetric. Hemodynamic AS severity is independently affected not only by global AVC but also by variations in AVC distribution and location within cusps, emphasizing the importance of 4D AVC assessment in AS. Impact of quantified AVC asymmetry and location on outcome of transcutaneous aortic valve replacement should be evaluated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".