Bibliographic record
Abstract
Objective: To advance robotic surgery simulation in gynecologic oncology by: (1) conducting a randomized controlled trial of a virtual reality, proficiency-based robotic simulation curriculum to teach robotic suturing and (2) developing an inanimate model for training in the procedure of robotic pelvic lymphadenectomy. Methods: (1) Residents and attending surgeons in surgical specialties were randomized to a proficiency-based virtual reality robotic simulation curriculum or usual clinical work over 5 weeks. Ability to perform robotic suturing of an inanimate model of the vaginal cuff with the actual da Vinci® robot was assessed at baseline and after the intervention period. (2) Using anatomic texts and virtual model of the anatomy of the human pelvis, an inanimate model for training in robotic pelvic lymphadenectomy was developed using low-cost materials. The model was robotically dissected by three gynecologic oncologists and two gynecologic oncology fellows all of whom rated the model post-dissection using a structured rating scale.Results: (1) As compared to controls, the training group showed a trend to greater improvement in ability to perform robotic suturing of an inanimate model of the vaginal cuff as assessed by a global rating scale and a statistically significantly greater improvement in total knots performed. (2) The pelvic lymphadenectomy model was rated favorably for both anatomic realism and potential as a training tool. Discussion: Participation in a virtual reality robotic surgery training curriculum shows a trend to improving ability to perform inanimate tasks with the actual da Vinci® robot, suggesting a translational benefit. A high fidelity training model of robotic pelvic lymphadenectomy has been developed using low-cost materials. Both projects help to advance surgical simulation in gynecologic oncology, the first at the level of basic task training and the second in advanced techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".