Impact of FIGO 2023 staging criteria on stage migration and survival outcomes in early-stage endometrial cancer: A retrospective cohort study
Bibliographic record
Abstract
Objective To compare the revised FIGO 2023 endometrial cancer (EC) staging system to the previous FIGO 2009 schema, assessing stage migration and prognostic capability. Methods Patients who underwent EC surgical staging (including sentinel lymph node biopsy or lymphadenectomy) between May 2015 and July 2023 were restaged from FIGO 2009 to FIGO 2023 ( n = 538). Overall survival (OS) and progression-free survival (PFS) for substages were estimated using the Kaplan-Meier method and compared using log-rank test. Results Stage migration occureed in 26.8 % of cases ( n = 144) with almost all upstaged ( n = 143, 99.3 %). Upstaging included migration from stage IA to II (97/301, 32.2 %) and stage IB to II (46/85, 54.1 %). No significant differences in OS or PFS were noted between FIGO 2023 stage II substages (IIA vs. IIB vs. IIC vs. IICm p53abn ; p OS = 0.53 and p PFS = 0.59). When FIGO 2023 stage IIC and IICm p53abn were stratified by initial FIGO 2009 substages (52 IIC: 23 IA, 21 IB, 8 II; and 101 IICm p53abn : 67 IA, 17 IB, 17 II), significant differences in OS and PFS were identified between FIGO 2009 substages IA vs. IB vs. II ( p < 0.05). Conclusions The FIGO 2023 EC staging revision has resulted in significant upstaging from IA/IB to II, due to incorporation of lymphovascular space invasion, histology, and molecular profiles. No significant differences in survival were found between FIGO 2023 stage II substages, suggesting lack of discriminatory ability. Significant survival differences were seen within stages IIC and IICm p53abn when stratified by initial FIGO 2009 substages, suggesting FIGO 2023 IIC and IICm p53abn are heterogeneous cohorts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".