Novel cardiac CT method for identifying the atrioventricular conduction axis by anatomic landmarks
Bibliographic record
Abstract
BACKGROUND: Understanding the conduction axis location aids in avoiding iatrogenic damage and guiding targeted heart rhythm therapy. OBJECTIVE: Cardiac structures visible with clinical imaging have been demonstrated to correlate with variability in the conduction system course. We aimed to standardize and assess the reproducibility of predicting the location of the atrioventricular conduction axis by cardiac computed tomography. METHODS: We evaluated 477 patients with acquired aortic valve disease by cardiac computed tomography to assess variability in cardiac structures established to relate to the conduction system. We standardized 3 points (points A-C) to estimate the course from the atrioventricular node to the nonbranching bundle and left bundle branch origin and further compared this with measures of variability in the aortic root and membranous septum. RESULTS: The mean distances between the aortic valve virtual basal ring and points A, B, and C were 9.5 ± 3.5 (0.3-20.1) mm, 5.0 ± 2.6 (-1.7 to 15.9) mm, and 2.9 ± 2.5 (-5.2 to 12.0) mm, respectively. The midpoint of the membranous septum deviated posteriorly a median of -4.4 (interquartile range, -12.4 to +3.0) degrees relative to the commissure between the right coronary and noncoronary leaflets. Intraclass coefficients for both intraobserver and interobserver variability for all measured points were excellent (≥0.78). CONCLUSION: These findings further infer the intimate yet highly variable relationship between the conduction axis and aortic root. This reproducible and standardized approach needs validation in populations of patients requiring accurate identification of the atrioventricular components of the conduction axis, which may serve as a noninvasive means for estimating its location.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".