A Surgery-based Comprehensive Treatment Improved Prognosis in Patients with Stage IIIC Cervical Squamous Carcinoma: A Single Center Retrospective Study
Bibliographic record
Abstract
Objective: This study intended to analyze the prognosis of patients with stage IIIC squamous cervical cancer who underwent the Obstetrics and Gynecology Hospital of Fudan University (FUOG) Treatment, and explored the factors influencing their prognosis. Design: A retrospective study. Setting: A large tertiary hospital specializing in obstetrics and gynecology in China. Population or Sample: This study collected data from 717 patients with stage IIIC squamous cervical cancer who underwent FUOG Treatment in our hospital from January 2016 to December 2020. Methods: Kaplan-Meier method was used to estimate progression-free survival (PFS) and overall survival (OS). Stratified analysis was performed to examine the risk factors. Main Outcome Measures: The main outcomes were 3-year PFS and OS. Results: The 3-year OS was 90.9% for patients with stage IIIC squamous cervical cancer, 91.5% for stage IIIC1 and 83.2% for stage IIIC2, respectively. The 3-year PFS was 84.8%, 85.3% for stage IIIC1 and 78.8% for stage IIIC2, respectively. Undifferentiated squamous carcinoma was an independent prognostic factor for OS (HR: 5.793, p=0.0064) and PFS (HR: 4.663, p=0.0033). Postoperative patients with standard adjuvant therapy had better 3-year OS outcomes than patients with non-standard therapy (88.4% vs 73.4%, p=0.007). Patients with undifferentiated type (OR=8.471), positive parietal infiltration (OR=3.339), or tumor infiltration depth of 1/3-2/3 (OR=5.454) were more likely to have distant recurrence. Conclusions: The prognosis of patients with stage IIIC cervical squamous carcinoma treated with the FUOG Treatment is satisfactory. However, risk factors such as undifferentiated type, positive paracervical infiltration, and non-standard adjuvant therapy can negatively affect prognosis.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".