Maintaining technical proficiency in senior surgical fellows during the COVID-19 pandemic through virtual teaching
Bibliographic record
Abstract
Background The novel coronavirus (COVID-19) pandemic has resulted in a severe reduction in operative opportunities for trainees. We hypothesized that augmenting independent practice with a bench model of vascular anastomoses using regular videoconferences and individual feedback would provide a meaningful benefit in the maintenance of technical skills in senior lung transplant surgical fellows. Methods A lung transplantation virtual technical skills course was developed, and surgical fellows were provided with a bench model and surgical instruments. Using a virtual communication platform, teaching sessions were held twice weekly, and fellows performed an anastomosis on camera. Video recordings were reviewed and critiqued by attending staff. At the end of the 3-month course, participants were surveyed about their experience. Warm ischemic time was compared between the fellows' 5 most recent cases before and after the pandemic. Results Seven senior surgical fellows participated and provided feedback. The fellows had graduated medical school an average of 14 years before fellowship, and spent an average of 5 hours (range, 1.3-15 hours) of home practice. Five of the 7 participants (71%) reported improvement in their technical skills and increased confidence in performing lung transplantation. No significant difference in average warm ischemic time in procedures performed by fellows was identified (70.3 minutes prepandemic vs 68.3 minutes postpandemic; P = .68). Conclusions A program of virtual technical skills teaching, individual video coaching, and independent practice has provided a benefit in maintaining technical skills in lung transplant surgical fellows during the COVID-19 pandemic, when equivalent operative experience was unavailable. Lessons learned from this exceptional time can be used to create simulation curricula for senior trainees.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".