Assessment of Five-Year Relative Survival of Patients With Endometrial Cancer: A Period Analysis
Bibliographic record
Abstract
Background: Endometrial cancer is one of the most common female cancers globally and in China. Although timely assessment of 5-year relative survival is crucial for guiding secondary prevention and early screening programs for endometrial cancer patients, those kinds of data are scarce in China. We aimed to provide a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from eastern China. Methods: Overall, 945 patients diagnosed with endometrial cancer during 2004 - 2018 from four cancer registries with high-quality data from Taizhou, eastern China were included. Period analysis was used to calculate 5-year relative survival for overall and the stratification by age at diagnosis and region. Model-based period analysis was used to predict the 5-year relative survival for the upcoming period of 2019 - 2023. Results: We found that 5-year relative survival during 2014 - 2018 reached 86.4% for overall, while urban areas had higher survival compared to rural areas (91.3% vs. 85.3%). Furthermore, there was a clear age gradient, decreasing from 89.3% for age < 55 years to 80.5% for age > 74 years. Predicted 5-year relative survival for the upcoming period 2019 - 2023 could reach 88.4%. Conclusions: We provide, a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from Taizhou, eastern China, reaching 86.4% for overall. Our finding has important implications for the overall evaluation of early detection and screening programs for patients with endometrial cancer in eastern China.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".