An over-view of robot assisted surgery curricula and the status of their validation
Bibliographic record
Abstract
INTRODUCTION: Robotic surgery is a rapidly expanding field. Thus far training for robotic techniques has been unstructured and the requirements are variable across various regions. Several projects are currently underway to develop a robotic surgery curriculum and are in various stages of validation. We aimed to outline the structures of available curricula, their process of development, validation status and current utilization. METHODS: We undertook a literature review of papers including the MeSH terms "Robotics" and "Education". When we had an overview of curricula in development, we searched recent conference abstracts to gain up to date information. RESULTS: The main curricula are the FRS, the FSRS, the Canadian BSTC and the ERUS initiative. They are in various stages of validation and offer a mixture of theoretical and practical training, using both physical and simulated models. DISCUSSION: Whilst the FSRS is based on tasks on the RoSS virtual reality simulator, FRS and BSTC are designed for use on simulators and the robot itself. The ERUS curricula benefits from a combination of dry lab, wet lab and virtual reality components, which may allow skills to be more transferable to the OR as tasks are completed in several formats. Finally, the ERUS curricula includes the OR modular training programme as table assistant and console surgeon. CONCLUSION: Curricula are a crucial step in global standardisation of training and certification of surgeons for robotic surgical procedures. Many curricula are in early stages of development and more work is needed in development and validation of these programmes before training can be standardised.
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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 | 0.065 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".