Outcome of uterine clear cell carcinomas compared to endometrioid carcinomas and poorly-differentiated endometrioid carcinomas.
Bibliographic record
Abstract
OBJECTIVES: Our aim was to compare the survival between patients with clear cell carcinoma (CC) and patients with endometrioid carcinoma (EC). METHODS: Through the population-based Geneva Cancer Registry, we identified 1,380 resident women diagnosed with uterine cancer between 1970 and 2000. We excluded those with papillary serous endometrial carcinoma and uterine sarcomas. We categorized patients as CC (n = 32, 2.8%) or EC (n = 1,145, 97.2%). Uterine cancer-specific survival rates were calculated by Kaplan-Meier analysis. We used Cox proportional hazards analysis to compare uterine cancer mortality risks between groups, and adjusted these risks for other prognostic factors. RESULTS: CC patients presented with a more advanced stage at diagnosis than EC patients (p = 0.002). Compared to women with EC, women with CC had a significantly greater risk of dying from their disease (hazard ratio [HR] 2.9, 95% confidence interval (95% CI) 1.7-4.9). After adjustment for age, stage and adjuvant chemotherapy, the risk of dying from uterine cancer was still significantly higher for CC patients (HR 2.0, 95% CI 1.2-3.4). By univariate analysis, the risk of dying of endometrial cancer was not significantly higher in CC patients than in patients with poorly-differentiated EC (HR 1.3, 95% CI 0.7-2.3). CONCLUSION: This population-based investigation shows that patients with CC have a poorer outcome than those with EC. Studies to determine the role of adjuvant treatment in CC patients are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".