Risk factors for second primary cancer in a prospective cohort of endometrial cancer survivors: an Alberta Endometrial Cancer Cohort Study
Bibliographic record
Abstract
We examined associations between modifiable and nonmodifiable cancer-related risk factors measured at endometrial cancer diagnosis and during early survivorship (~3 years postdiagnosis) with second primary cancer (SPC) risk among 533 endometrial cancer survivors in the Alberta Endometrial Cancer Cohort using Fine and Gray subdistribution hazard models. During a median follow-up of 16.7 years (IQR, 12.2-17.9), 89 (17%) participants developed an SPC; breast (29%), colorectal (13%), and lung (12%) cancers were the most common. Dietary glycemic load before endometrial cancer diagnosis (≥90.4 vs < 90.4 g/day: subhazard ratio [sHR] = 1.71; 95% CI, 1.09-2.69), as well as older age (≥60 vs < 60 years: sHR = 2.48; 95% CI, 1.34-4.62) and alcohol intake (≥2 drinks/week vs none: sHR = 3.81; 95% CI, 1.55-9.31) during early survivorship, were associated with increased SPC risk. Additionally, reductions in alcohol consumption from prediagnosis to early survivorship significantly reduced SPC risk (sHR = 0.34; 95% CI, 0.14-0.82). With 1 in 6 survivors developing an SPC, further investigation of SPC risk factors and targeted surveillance options for high-risk survivors could improve long-term health outcomes in this population. Reductions in dietary glycemic load and alcohol intake from prediagnosis to early survivorship showed promising risk reductions for SPCs and could be important modifiable risk factors to target among endometrial cancer survivors. This article is part of a Special Collection on Gynecological Cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".