Comparison of three surgical approaches for staging lymphadenectomy in high‐risk endometrial cancer
Bibliographic record
Abstract
OBJECTIVE: To compare laparotomy, laparoscopy, and robotic surgical approaches to lymphadenectomy for high-risk endometrial cancer staging. METHODS: A retrospective cohort study enrolled patients who underwent surgery for pathologic high-risk endometrial carcinoma at the University Health Network, Toronto, Canada, between January 1, 2005 and December 31, 2013. The primary outcome, the median number of nodes retrieved, was compared based on surgical technique. The secondary outcome was the detection of metastatic nodes. RESULTS: A total of 176 patients who underwent surgery for high-risk endometrial cancer were included, of whom 147 (83.5%) had pelvic and 78 (44.3%) had para-aortic lymphadenectomy. Laparotomy, laparoscopy, and robotic approaches were applied for 69 (39.2%), 44 (25.0%), and 63 (35.8%) patients, respectively. Minimally-invasive staging was associated with an increased proportion of patients undergoing pelvic lymphadenectomy compared with laparotomy (P=0.005). The median number of nodes removed in the pelvis and para-aortic regions did not differ between surgical approaches. The detection of metastatic nodes was also similar between the groups. Increased blood loss (P<0.001) and longer hospital admission (P<0.001) were observed with laparotomy procedures. CONCLUSION: All three techniques demonstrated adequate staging of high-risk endometrial carcinoma. Based on improved peri-operative outcomes, the use of minimally-invasive techniques is advocated where appropriate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".