An over-view of robot assisted surgery curricula and the status of their validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it