Certification in echocardiography of congenital heart disease: experience of the first 6 years of a European process
Bibliographic record
Abstract
AIMS: Certification in congenital heart disease echocardiography presents unique challenges compared with certification of adult transthoracic and transoesophageal echocardiography. We report our experience in collaboratively developing an exam process that covers the size and age range of congenital heart patients, the varying professional backgrounds of echocardiography practitioners across the field and our approach to the challenge of introducing a pan-European certification endorsed by the major stakeholder groups; the European Association of Echocardiography (EAE), the Association for European Paediatric and Congenital Cardiology (AEPC) and the Grown Up Congenital Heart Working Group of the European Society of Cardiology (ESC). METHODS AND RESULTS: Since its inception in 2006 the exam has been held seven times; 137 candidates from 27 countries have sat the exam, 107 candidates (78%) have passed the exam components and 60 candidates have successfully completed the logbook submission and have been certified in echocardiography of congenital heart disease echocardiography by the EAE. In addition to the certification process, a comprehensive curriculum, teaching programme, and teaching courses have been developed. CONCLUSION: The instititution of a European certification process for echocardiography of congenital heart disease has proved feasible.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".