The Role of Virtual Realty in the Changing Landscape of Surgical Training
Bibliographic record
Abstract
Background: The current coronavirus disease 2019 pandemic has caused unprecedented challenges to surgical training across the world. With the widespread cancellations of clinical and academic activities, educators are looking to technological advancements to help 'bridge the gap' and continue medical education. Solutions: Simulation-based training as the 'gold standard' for medical education has limitations that prevent widespread adoption outside suitably resourced centres. Virtual reality has the potential to surmount these barriers, whilst fulfilling the fundamental aim of simulation-based training to provide a safe, effective and realistic learning environment. Current limitations and insights for future: The main limitations of virtual reality technology include comfort and the restrictive power of mobile processors. There exists a clear developmental path to address these restrictions. Continued developments of the hardware and software set to deepen immersion and widen the possibilities within surgical education. Conclusion: In the post coronavirus disease 2019 educational landscape, virtual, augmented and mixed reality technology may prove invaluable in the training of the next generation of surgeons.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".