Assessment of left atrial fibrosis progression in canines following rapid ventricular pacing using 3D late gadolinium enhanced CMR images
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is associated with extracellular matrix (ECM) remodelling and often coexists with myocardial fibrosis (MF); however, the causality of these conditions is not well established. OBJECTIVE: We aim to corroborate AF to MF causality by quantifying left atrial (LA) fibrosis in cardiac magnetic resonance (CMR) images after persistent rapid ventricular pacing and subsequent AF using a canine model and histopathological validation. METHODS: Twelve canines (9 experimental, 3 control) underwent baseline 3D LGE-CMR imaging at 3T followed by insertion of a pacing device and 5 weeks of rapid ventricular pacing to induce AF (experimental) or no pacing (control). Following the 5 weeks, pacing devices were removed to permit CMR imaging followed by excision of the hearts and histopathological imaging. LA myocardial segmentation was performed manually at baseline and post-pacing to permit volumetric %MF quantification using the image intensity ratio (IIR) technique, wherein fibrosis was defined as pixels > mean LA myocardium intensity + 2SD. RESULTS: Volumetric %MF increased by an average of 2.11 ± 0.88% post-pacing in 7 of 9 experimental dogs. While there was a significant difference between paired %MF measurements from baseline to post-pacing in experimental dogs (P = 0.019), there was no significant change in control dogs (P = 0.019 and P = 0.5, Wilcoxon signed rank tests). The median %MF for paced animals was significantly greater than that of non-paced dogs at the 5-week post-insertion time point (P = 0.009, Mann Whitney U test). Histopathological imaging yielded an average %MF of 19.42 ± 4.80% (mean ± SD) for paced dogs compared to 1.85% in one control dog. CONCLUSION: Persistent rapid ventricular pacing and subsequent AF leads to an increase in LA fibrosis volumes measured by the IIR technique; however, quantification is limited by inherent image acquisition parameters and observer variability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".