Impact of the Coronavirus Disease 2019 Pandemic on Cardiac Surgical Education in North America
Bibliographic record
Abstract
OBJECTIVE: We report the impact of the coronavirus disease 2019 (COVID-19) pandemic on cardiac surgery trainee education in North America. METHODS: A survey was sent to participating academic adult cardiac surgery centers in North America. Data regarding the effect of COVID-19 on cardiac surgery training were analyzed. RESULTS: = 33) of patients hospitalized with COVID-19. The majority of institutions have converted didactics (high burden 90% vs low burden 73%) and interviews for jobs/fellowships (high burden 75% vs low burden 73%) from in-person to virtual. Institutions were mixed in preference for administration of the licensing examination, with the most common preference for examinations to be held remotely on normal timeline (high burden 45% vs low burden 30%) or in person with more than 3-month delay (high burden 20% vs low burden 33%). Despite the challenges experienced during the COVID-19 pandemic on trainee clinical experience, re-deployment, and decreased operative volume, institutions expected their trainees to graduate on schedule (high burden 95% vs low burden 91%). CONCLUSIONS: Our study demonstrates that actions taken during the COVID-19 pandemic has led to disruptions in cardiac surgery training with transition of didactics and interviews virtually and re-deployment to alternative duties. Despite this, institutions remain optimistic that their trainees will graduate on schedule.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".