Oncologic and Operative Outcomes of Robotic Staging Surgery Using Low Pelvic Port Placement in High-Risk Endometrial Cancer
Bibliographic record
Abstract
Upper para-aortic lymph node dissection (PALND) is one of the most challenging gynecologic robotic procedures. This study aimed to evaluate the oncologic and operative outcomes of robotic staging surgery, including upper PALND, using low pelvic port placement (LP3) in 22 patients with high-risk endometrial cancer. High-risk was defined as patients who showed deep myometrial invasion with grade III, cervical involvement, or high-risk histology. The mean patient age and body mass index were 58 years and 24 kg/m2. The mean operative time was 263 min. The mean number of total LNs and upper PALNs obtained was 31 and 10. Two patients received lymphangiography to reduce the amount of drained lymphatic fluid after surgery. The recurrence rate was 13.6% (3/22). There were two LN recurrences and one at the peritoneum in the intra-abdominal cavity. Robotic staging surgery using LP3 was feasible for performing PALND as well as procedures in the pelvic cavity simultaneously. It provides important techniques for performing optimal surgical procedures when surgeons decide to perform comprehensive PALND in instances of isolated recurrence or unexpected LN enlargement as well as high-risk endometrial cancer. Consequently, surgeons can achieve surgical consistency and reproducibility for PALND, leading to improved operative and survival outcomes in high-risk endometrial cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".