Hormone Use After Nonserous Epithelial Ovarian Cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether hormone therapy (HT) after nonserous epithelial ovarian cancer is associated with a decrease in overall and disease-free survival. METHODS: We conducted a retrospective cohort study. The Manitoba Cancer Registry and Drug Programs Information Network were searched to find all women with known nonserous epithelial ovarian, fallopian tube, or primary peritoneal cancer between 1995 and 2010 who had used HT after treatment. Women who did not receive treatment or had no follow-up were excluded. RESULTS: Three hundred ninety-one patients met the inclusion criteria. Seventeen patients were excluded because the patients did not receive treatment for cancer, and 17 were excluded for lack of follow-up. A total of 94 women received HT after treatment, and 263 women did not. The average age was 57.8 years. In HT users younger than 55 years of age, disease-free survival is improved according to both the multivariable landmark analysis (n=68/145, adjusted hazard ratio 0.354, 95% confidence interval [CI] 0.17-0.74, P=.006) and the time-varying Cox regression analysis (n=42/158, adjusted hazard ratio 0.212, 95% CI 0.07-0.60, P=.004) when adjusting for International Federation of Gynecology and Obstetrics stage and need for chemotherapy. There is no statistical difference in overall survival in this age group. No associations between HT use and overall survival or disease-free survival were found among women aged 55 years and older. CONCLUSION: After treatment for nonserous epithelial ovarian cancer, hormone therapy is not associated with decreased disease-free or overall survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".