Echocardiography training for cardiac surgery residents: results of a Canadian needs assessment
Bibliographic record
Abstract
BACKGROUND: In assessing an unstable patient post cardiac surgery, echocardiography can be an essential tool as part of this assessment. However, it may be under-utilized for several reasons. We conducted this study to determine the perceived needs and training objectives for echocardiography training for cardiac surgery residents. METHODS: This study was a cross-sectional, stratified national survey of cardiac surgery residents, cardiac surgeons, cardiac surgery program directors and cardiologists, designed to acquire opinions on what type and level of objective-based training in echocardiography is required for cardiac surgery residents. Recruitment of survey subjects was through Fluid Surveys email invitations to 201 physicians. Participants were asked to rate the importance of focused echocardiography training for cardiac surgery residents and also give a grade of importance for 18 training objectives. RESULTS: We received 89 completed surveys. More than 80 % of responders feel that echocardiography training should be required for cardiac surgery residents. Forty seven percent of all responders felt that residents should take an echocardiography course with or without a rotation to train in echocardiography. Thirty five percent felt that current training in most programs, which entails a single rotation in echocardiography, is sufficient. Seven training objectives were identified as important by more than 80 % of participants. CONCLUSION: Study participants believe that cardiac surgery residents require echocardiography training. The majority agree that echocardiography training should be informed by the identified 7 training objectives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".