2023 CCS/CSE Standards for Physician Training and Maintenance of Competence in Adult Echocardiography: Executive Summary
Bibliographic record
Abstract
Echocardiography is an imaging modality essential to the provision of contemporary cardiovascular care. Since the publication of the 2010 CCS/CSE Guidelines, 1 Burwash IG, Basmadjian A, Bewick D, Choy JB, Cujec B, Jassal DS, MacKenzie S, Nair P, Rudski L, Yu E, Tam JW. 2010 Canadian Cardiovascular Society/Canadian Society of Echocardiography Guidelines for Training and Maintenance of Competency in Adult Echocardiography. Can J Cardiol 2010;27:862-864. Google Scholar clinical echocardiography has expanded with respect to both technologies and applications. At the same time there has been a shift in emphasis away from quantity of training (duration and case volumes) to actual demonstration of competency to determine successful completion of training. 2 Royal College of Physicians and Surgeons of Canada “Competency by Design: Reshaping Medical Education” 2014 https://www.royalcollege.ca/rcsite/documents/educational-strategy-accreditation/royal-college-competency-by- design-ebook-e.pdf Accessed 8 March 2023. Google Scholar Concurrently, there is growing evidence of considerable variability in echocardiography competence among general cardiology trainees. 3 Nair P. Siu S.C. Sloggett C.E. Biclar L. Sidhu R.S. Yu E.H.C. The assessment of technical and interpretative proficiency in echocardiography. J Am Soc Echocardiogr. 2006; 19: 924-931 Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar , 4 Bierowski M.J. Qureshi U. Ramedani S. Grewal S. Shah R. Park B. Peterson B.R. Variable exposure to echocardiography core competencies when applying minimum recommended procedural numbers of cardiology fellows in training. Cardiovasc Ultrasound. 2022; 20: 24-32 Crossref Scopus (0) Google Scholar , 5 Yu C.C.W. Pendrith C. Chun S. Echocardiographic interpretation skills of graduating cardiology trainees. Can J Cardiol. 2022; 38: 1304-1306 Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar
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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 | 0.075 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.018 |
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".