Reliability of comprehensive echocardiography evaluation of patent ductus arteriosus among extremely preterm neonates across a national network: A prospective observational study
Bibliographic record
Abstract
BackgroundPrior studies on the reliability of targeted neonatal echocardiography (TNE) among extremely low gestational age neonates (ELGANs) with a patent ductus arteriosus (PDA) have been limited to evaluation of pre-defined images by a small set of study observers. The objective of this study was to investigate the interobserver reliability of comprehensive echocardiography measures of PDA size/shunt volume and ventricular performance among TNE-neonatologists in a large national network.MethodsWe conducted a prospective observational study. TNEs performed for the evaluation of PDA among ELGANs were analyzed by TNE-neonatologists from the Canadian TNE Consortium. Analyses were conducted via an interactive videoconferencing platform offering full remote control to the review software. Reliability for continuous measures was evaluated using the intraclass correlation coefficient (ICC) and coefficient of variation. The kappa statistic was used to evaluate the interobserver reliability of categorical parameters.ResultsReliability was excellent among indices of PDA size and gradient (ICC≥0.91) and good-to-excellent among most indices of left ventricular (LV) size and output (ICC≥0.79, except for LV end-systolic volume and left atrium to aortic root ratio). There was substantial to near-complete agreement on PDA shunt direction and diastolic flow abnormalities in the abdominal aorta and systemic arteries (kappa ≥0.78). However, reliability for measures of LV systolic and diastolic performance was variable with ICC range 0.21-0.95, though with low coefficient of variation (<15%).ConclusionsInterobserver reliability for most TNE measures of PDA size, gradient, shunt volume, and LV dimensions and function is good-to-excellent, supporting the validity of incorporating these indices in prospective multicenter research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".