Quantification of Women Who Could Benefit from Hormone Therapy after Endometrial Cancer Treatment: An Analysis of SEER Data
Bibliographic record
Abstract
Our primary aim was to estimate the magnitude of stage I endometrial cancer (EC) survivors that could benefit from hormonal therapy (HT). Our secondary aims were to assess EC incidence in women below 50 and below 60 over the years, and analyze the overall survival and any influencing factors. We analyzed the endometrioid EC data from the Surveillance, Epidemiology, and End Results (SEER) program according to women's age, tumor stage, and grade. We analyzed the proportions of EC survivors below 50 and below 60 years of age and stratified those age groups by race. For age distribution and survival analysis SEER, 18 registries' research data (2000-2018) were analyzed. We analyzed the SEER 12 registries' research data (1992-2019) for incidence time trends. Our investigation found a 14% and 40% cumulative prevalence of stage I EC that occurs in women below 50 or 60 years, respectively. EC's prevalence has progressively risen in recent decades, but cancer-specific mortality remains low. The increasing number of women affected by EC in premenopause or early postmenopause face an 18 years-survival rate of 96.86% and 95.73%, respectively. A significant proportion of low-grade EC survivors can potentially benefit from HT treatment, and this requires awareness of other aspects of their health or quality of life, in addition to cancer treatments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".