Reclassification of serous ovarian carcinoma by a 2‐tier system
Bibliographic record
Abstract
BACKGROUND: A study was undertaken to use the 2-tier system to reclassify the grade of serous ovarian tumors previously classified using the International Federation of Gynecology and Obstetrics (FIGO) 3-tier system and determine the progression-free survival (PFS) and overall survival (OS) of patients treated on Gynecologic Oncology Group (GOG) Protocol 158. METHODS: The authors retrospectively reviewed demographic, pathologic, and survival data of 290 patients with stage III serous ovarian carcinoma treated with surgery and chemotherapy on GOG Protocol 158, a cooperative multicenter group trial. A blinded pathology review was performed by a panel of 6 gynecologic pathologists to verify histology and regrade tumors using the 2-tier system. The association of tumor grade with PFS and OS was assessed. RESULTS: Of 241 cases, both systems demonstrated substantial agreement when combining FIGO grades 2 and 3 (overall agreement, 95%; kappa statistic, 0.68). By using the 2-tier system, patients with low-grade versus high-grade tumors had significantly longer PFS (45.0 vs 19.8 months, respectively; P = .01). By using FIGO criteria, median PFS for patients with grade 1, 2, and 3 tumors was 37.5, 19.8, and 20.1 months, respectively (P = .07). There was no difference in clinical outcome in patients with grade 2 or 3 tumors in multivariate analysis. Woman with high-grade versus low-grade tumors demonstrated significantly higher risk of death (hazard ratio, 2.43; 95% confidence interval, 1.17-5.04; P = .02). CONCLUSIONS: Women with high-grade versus low-grade serous carcinoma of the ovary are 2 distinct patient populations. Adoption of the 2-tier grading system provides a simple yet precise framework for predicting clinical outcomes.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".