Education, training, and accreditation of Neonatologist Performed Echocardiography in Europe—framework for practice
Bibliographic record
Abstract
There is a growing interest worldwide in using echocardiography in the neonatal unit to act as a complement to the clinical assessment of the hemodynamic status of premature and term infants. However, there is a wide variation in how this tool is implemented across many jurisdictions, the level of expertise, including the oversight of this practice. Over the last 5 years, three major expert consensus statements have been published to provide guidance to neonatologists performing echocardiography, with all recommending a structured training program and clinical governance system for quality assurance. Neonatal practice in Europe is very heterogeneous and the proximity of neonatal units to pediatric cardiology centers varies significantly. Currently, there is no overarching governance structure for training and accreditation in Europe. In this paper, we provide a brief description of the current training recommendations across several jurisdictions including Europe, North America, and Australia and describe the steps required to achieve a sustainable governance structure with the responsibility to provide accreditation to neonatologist performed echocardiography in Europe.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".