Advancing Surgical Simulation in Gynecologic Oncology
Bibliographic record
Abstract
INTRODUCTION: Pelvic lymphadenectomy is a key component of the surgical treatment of several gynecologic cancers and involves mastery of complex anatomic relationships. Our aim was to demonstrate that the anatomy relevant to robotic pelvic lymphadenectomy can be modeled using low-cost techniques, thereby enabling simulation focused on surgical dissection, a task that integrates technical skills and anatomic knowledge. METHODS: A model of pelvic lymphadenectomy was constructed through experimentation with several different materials and a number of prototypes. In the final version, blood vessels were simulated by rubber tubing stented with wire and lymph nodes by cotton balls. Adipose and areolar tissue were simulated by a gelatin solution poured into the model and then allowed to cool and semisolidify. Three gynecologic oncologists and 2 gynecologic oncology fellows dissected the model using the surgical robot (da Vinci Surgical System) and completed a structured questionnaire. Five additional gynecologic oncologists assessed the model at a national conference. RESULTS: The model received high ratings for face and content validity. Median ratings were almost all 4 of 5 or higher (range, 3-5). Participants who dissected the model (n = 5) unanimously rated it as "useful for training throughout residency and fellowship." CONCLUSIONS: A novel low-cost inanimate model of pelvic lymphadenectomy has been developed and rated highly for face and content validity. This model may permit more regular simulation sessions compared with alternatives such as cadaveric dissection and animal laboratories, thereby complementing them and facilitating distributed practice.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".