Leveraging Videoconferencing Technology to Augment Surgical Training During a Pandemic
Bibliographic record
Abstract
Our objective was to review the use of videoconferencing as a practical tool for remote surgical education and to propose a model to overcome the impact of a pandemic on resident training. Summary Background Data: In response to the coronavirus disease 2019 pandemic, most institutions and residency programs have been restructured to minimize the number of residents in the hospital as well as their interactions with patients and to promote physical distancing measures. This has resulted in decreased resident operative exposure, responsibility, and autonomy, hindering their educational goals and ability to achieve surgical expertise necessary for independent practice. Methods: We conducted a narrative review to explore the use of videoconferencing for remote broadcasting of surgical procedures, telecoaching using surgical videos, telesimulation for surgical skills training, and establishing a didactic lecture series. Results and Conclusions: We present a multimodal approach for using practical videoconferencing tools that provide the means for audiovisual communication to help augment residents' operative experience and limit the impact of self-isolation, redeployment, and limited operative exposure on surgical training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".