Secondary Cancer Prevention During Follow-up for Endometrial Cancer
Bibliographic record
Abstract
OBJECTIVE: To conduct a population-based study of incidence and screening rates for secondary breast and colorectal cancers after endometrial cancer and to assess implications for follow-up. METHODS: This was a retrospective population-based study from administrative databases. The inception cohort included all women diagnosed with endometrial cancer in Ontario, Canada from 1996 to 2000, without a previous history of breast or colorectal cancer. We ascertained 5-year recurrence and overall survival rates and practitioner type during follow-up. Primary outcomes were age-standardized incidence and screening rates of breast and colorectal cancer during follow-up compared with the general female population. RESULTS: There were 3,473 women in the cohort. The 5-year recurrence rate was 15.0% and overall survival was 79.3%. Family physicians were most often involved in follow-up care. Age-standardized incidence rates of breast and colorectal cancer were 0.5% and 0.7%, respectively, compared with 0.5% (P=.76) and 0.2% (P<.001) in the general population. Age-standardized screening rates for these cancers were 64.0% and 30.0%, respectively, compared with 31.0% (P<.008) and 15.0% (P<.001) in the general population. Women aged older than 70 years and those with the lowest income were least likely to have secondary cancer screening. CONCLUSION: Women with endometrial cancer have a comparable risk of breast cancer but higher risk of colorectal cancer compared with the general population. Follow-up after endometrial cancer should include counseling and uptake of secondary cancer prevention strategies, which will contribute to maximizing long-term survivorship for these women. LEVEL OF EVIDENCE: II.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".