Cardiac surgeons' concerns, perceptions, and responses during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on health care and cardiac surgery. We report cardiac surgeons' concerns, perceptions, and responses during the COVID-19 pandemic. METHODS: A detailed survey was sent to recruit participating adult cardiac surgery centers in North America. Data regarding cardiac surgeons' perceptions and changes in practice were analyzed. RESULTS: Our study comprises 67 institutions with diverse geographic distribution across North America. Nurses were most likely to be redeployed (88%), followed by advanced care practitioners (69%), trainees (28%), and surgeons (25%). Examining surgeon concerns in regard to COVID-19, they were most worried with exposing their family to COVID-19 (81%), followed by contracting COVID-19 (68%), running out of personal protective equipment (PPE) (28%), and hospital resources (28%). In terms of PPE conservation strategies among users of N95 respirators, nearly half were recycling via decontamination with ultraviolet light (49%), followed by sterilization with heat (13%) and at home or with other modalities (13%). Reuse of N95 respirators for 1 day (22%), 1 week (21%) or 1 month (6%) was reported. There were differences in adoption of methods to conserve N95 respirators based on institutional pandemic phase and COVID-19 burden, with higher COVID-19 burden institutions more likely to resort to PPE conservation strategies. CONCLUSIONS: The present study demonstrates the impact of COVID-19 on North American cardiac surgeons. Our study should stimulate further discussions to identify optimal solutions to improve workforce preparedness for subsequent surges, as well as facilitate the navigation of future healthcare crises.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".