Long-term outcomes in patients with endometrial cancer after sentinel lymph node biopsy versus lymphadenectomy alone: a meta-analysis
Bibliographic record
Abstract
Objective: This study aimed to assess the prognosis of endometrial cancer (EC) patients after sentinel lymph node biopsy (SLNB) or lymph node dissection (LND) alone. Methods: EMBASE, PUBMED, COCHRANE, and WEB of SCIENCE were thoroughly searched for relevant articles until October 2024. The outcomes of interest encompassed overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Data analysis was made in STATA 18.0. The Newcastle-Ottawa Scale tool was leveraged to appraise study quality. Results: 13 studies were included, involving 36621 EC patients. No difference was revealed in OS between SLNB and LND (HR=1.04, 95%CI: 0.80-1.33; P=0.789). In subgroup analyses, the SLNB group from survival curves had worse OS (HR=1.63, 95%CI: 1.04-2.56; P=0.035); the SLNB group with intermediate- to high-risk EC had better OS (HR=0.20, 95%CI: 0.08-0.49; P<0.001). No difference was revealed in PFS between SLNB and LND (HR=0.99, 95%CI: 0.76-1.28; P=0.927). SLNB had better PFS in Asia (HR=0.44, 95% CI: 0.20-0.98, P=0.046) and stage I-III EC (HR=0.46, 95% CI: 0.24-0.89; P=0.021). No statistical difference was found in DSS (HR=3.18, 95%CI: 0.91-11.07; P=0.069). Conclusion: SLNB is an effective alternative to conventional LND in either low- or intermediate-high-risk EC patients. However, due to the retrospective nature of most included studies and the limited data on high-risk patients, further prospective randomized controlled trials are warranted to validate these findings. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024489323.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".