Abstract 2603: Predictors of Coronary Artery Visualization in Kawasaki Disease in the Pediatric Heart Network (PHN) Multi-Center Study
Bibliographic record
Abstract
Background: Echocardiography is the imaging modality of choice for evaluation of coronary artery (CA) abnormalities in Kawasaki disease (KD). Single center series have established high specificity and sensitivity for abnormality detection. Objective: To determine visualization rates of CAs across clinical centers and factors associated with success. Methods: Subjects enrolled in an 8 center PHN prospective, randomized trial of pulse steroids in primary treatment of KD underwent standardized echo evaluation at diagnosis and at 1 and 5 weeks later. All studies were interpreted by local centers and at a core laboratory (lab) which provided training and feedback over the study period. Imaging rates were calculated for each tertile of study time (9 month blocks). We explored univariate and multivariate predictors of CA visualization. Results: The core lab evaluated 587 echoes from 199 patients over 27 months. Left main, proximal/distal left anterior descending (LAD) and proximal right CAs were verified as visualized by the core lab in 91–98% of studies, but less often for the distal right (65%), circumflex (86%) and posterior descending (PD) (54%) CA segments. Visualization rates for 2 of the 3 less successfully imaged segments improved with time (p<.05). Weight and BSA did not impact success. In multivariate analysis, local center, CA segment and time from study start to echo were independent predictors of visualization (all p<.001). For CA segments for which % visualization varied by center, higher % visualization was associated with larger center volume (p=.001). Use of routine sedation was also associated with higher % visualization: distal LAD odds ratio (OR) 2.32 (95% CI 1.2, 4.49; p = 0.13), distal right OR 9.09 (95% CI 5.38, 15.35; p<.001), circumflex OR 2.94 (95% CI 1.61, 5.38; p<.001), PD OR 2.77 (95% CI 1.7, 4.53; p<.001). Moderate agreement between local and core lab readings (ICC 0.55– 0.70) was present for all CA segments except the PD (ICC 0.38). Conclusion: Successful visualization of CAs in KD is associated with the specific CA segment being evaluated, and is influenced by center volume and sedation use. Increased visualization rates over time suggest a learning curve and underscore the value of core lab oversight in pediatric multi-center trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".