Exploring the prognostic impact of tumor sidedness in ovarian cancer: A population-based survival analysis of over 10,000 patients
Bibliographic record
Abstract
Very recently, emerging evidence demonstrated that laterality might be an independent prognostic factor in patients with advanced ovarian cancer (OC). Based on preliminary provocative observations, our study aimed to investigate the prognostic impact of sidedness in a large cohort of women with OC. Survival was estimated based on Kaplan-Meier method and survival curves were compared using Log-rank test. Cox proportional-hazards model was used to study the association between survival and covariates. A total of 10,177 women with OC were included. Mean age at diagnosis was 59.58 years (±13.5); 36.7% OC right-sided, 36.9% were left- sided, and 26.4% had bilateral OC. The median overall survival (OS) for the entire population was 77 months, with the lowest median OS observed in bilateral OC (median OS: 34 months). The prognostic value of OC sidedness was not confirmed at the univariable analysis (HR = 0.958; 95% CI: 0.888-1.033, p = 0.268). However, women with bilateral OC has a 45% higher risk of death as compared with unilateral diagnosis (HR = 1.453; 95% CI: 1.410-1.497; p< 0.001). The independent prognostic value was further confirmed on multivarible analysis after adjusting for covariates including age, marital status, histological type, CA-125 at diagnosis, grade, stage, chemotherapy and surgery (HR = 1.087; 95% CI: 1.043-1.136, p = 0.02). However, the ultimate prognostic significance appeared less prominent, with bilateral OC conferring a relative increase of 8.7% of mortality. Our real-world study demonstrated that impact of tumor sidedness has no prognostic implication (right vs left OC) but bilateral OCs might be marginally more prognostically unfavorable. Prospective validation might be warranted, to confirm the prognostic significance of OC sidedness, including for the presence of key genetic alterations and lymph nodes asymmetry, to better stratify patients with OC and predict outcomes according to tumor sidedness at diagnosis.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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".