A Methodology to Assess Subregional Geometric Complexity for Tetralogy of Fallot Patients
Bibliographic record
Abstract
Abstract During surgical repair of tetralogy of fallot (TOF), pulmonary valve preservation (preservative repair) has demonstrated improved long-term outcomes compared to repairs that incise into the valve annulus (nonpreservative repair). Given the influence of geometry on hemodynamics, the success of preservative repair may be linked to the suitability of the preoperative patient geometry. However, the specific patient anatomies that may be predisposed to successful preservative repair are unknown due to significant interpatient variability in right ventricular outflow tract (RVOT) and pulmonary artery geometries, as well as the limitations in current methods of subregional geometric analysis. As a first step toward understanding the link between geometry and hemodynamics in TOF patients at a subregion level, we characterize the TOF geometry from the right ventricular infundibulum (INF) to the left and right pulmonary arteries. Our process consists of segmentation of magnetic resonance (MR) images and analysis of cross-sectional slices of the geometries along the centerlines. For the INF, main, left, and right pulmonary arteries individually, we quantify geometric parameters important in determining hemodynamic characteristics such as flow separation and recirculation, which can influence the degree of regurgitation. Specifically, we calculate the diameter along the subregion length, the average diameter, length, and tortuosity for each segment, as well as the bifurcation, left pulmonary artery (LPA) and right pulmonary artery (RPA) branch angles. This approach enables direct geometric comparisons within and among patients and allows for observation of the range in anatomic presentation. We have applied this approach to a dataset of 11 postoperative TOF patients, repaired with both preservative and nonpreservative surgical techniques.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".