Echocardiographic image collection and evaluation in infants with CHD: lessons learned from the imaging core lab for the Residual Lesion Score study
Bibliographic record
Abstract
core is a novel tool for assessing whether specific residual cardiac lesions after surgery have a measurable impact on outcome. The goal is to understand which residual lesions can be tolerated and which should be addressed prior to leaving the operating room. The Residual Lesion Score study is a large multicentre prospective study designed to evaluate the association of Residual Lesion Score to outcomes in infants undergoing surgery for CHD. This Pediatric Heart Network and National Heart, Lung, and Blood Institute-funded study prospectively enrolled 1,149 infants undergoing 5 different congenital cardiac surgical repairs at 17 surgical centres. Given the contribution of echocardiographic measurements in assigning the Residual Lesion Score, the Residual Lesion Score study made use of a centralised core lab in addition to site review of all data. The data collection plan was designed with the added goal of collecting image quality information in a way that would permit us to improve our understanding of the reproducibility, variability, and feasibility of the echocardiographic measurements being made. There were significant challenges along the way, including the coordination, de-identification, storage, and interpretation of very large quantities of imaging data. This necessitated the development of new infrastructure and technology, as well as use of novel statistical methods. The study was successfully completed, but the size and complexity of the population being studied and the data being extracted required more technologic and human resources than expected which impacted the length and cost of conducting the study. This paper outlines the process of designing and executing this complex protocol, some of the barriers to implementation and lessons to be considered in the design of future studies.
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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 | 0.169 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".