Risk Factors for Recurrence in Serous Borderline Ovarian Tumors and Early-Stage Low-Grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
BACKGROUND: Tumor recurrence significantly impacts the quality of life and fertility of patients with serous borderline ovarian tumors (SBOT) and early-stage low-grade serous ovarian carcinoma (LGSOC). This study aims to characterize recurrence patterns, identify independent risk factors for recurrence, and develop a nomogram to predict recurrence-free survival (RFS). METHODS: We conducted a retrospective case-control study to investigate recurrence in patients undergoing fertility-sparing surgery (FSS) and radical surgery (RS). Logistic regression and Cox regression were used to identify risk factors. Kaplan-Meier analysis was applied to evaluate RFS. A nomogram was developed based on identified variables to predict RFS. RESULTS: Tumor capsule disruption and micropapillary were associated with higher recurrence risk in the FSS group. Non-invasive implants were associated with higher recurrence risk in the RS group. The nomogram prediction model was developed based on identified risk factors. The area under the curve (AUC) for RFS predictions was 0.74 (95% CI: 0.62-0.85) at 3 years and 0.78 (95% CI: 0.67-0.89) at 5 years for the FSS group and 0.87 (95% CI: 0.76-0.98) at 3 years and 0.81 (95% CI: 0.65-0.97) at 5 years for the RS group. CONCLUSIONS: We identified the risk factors for recurrence of SBOT and early-stage LGSOC following FSS and RS procedures and developed a predictive model for forecasting RFS. This model provides valuable guidance for patients and clinicians in predicting recurrence risk for patients.
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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 | 0.006 |
| 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 | 0.000 | 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".