Anthropometric Measures and the Risk of Endometrial Cancer, Overall and by Tumor Microsatellite Status and Histological Subtype
Bibliographic record
Abstract
Obesity is an established risk factor for endometrial cancer, but this association is not well understood for subtypes of endometrial cancer. We evaluated the association of recent and adult-life obesity with subtypes of endometrial cancer based on microsatellite status (microsatellite-stable (MSS) vs. microsatellite-instable (MSI)) and histology (type I vs. type II). Analyses were based on a population-based case-control study (524 cases and 1,032 controls) conducted in Alberta, Canada (2002-2006) and included the following groupings of subtypes: MSS = 337 and MSI = 130; type I = 458 and type II = 66. Logistic and polytomous logistic regression were used to estimate odds ratios and 95% confidence intervals for overall endometrial cancer and subtypes of endometrial cancer, respectively. The risks of all subtypes of endometrial cancer, except type II, increased with an increase in all of the anthropometric characteristics examined. The risks for MSI tumors were suggestively stronger than those for MSS tumors; the risk with high (≥30) body mass index (weight (kg)/height (m)(2)) was significantly stronger for MSI tumors (odds ratio = 4.96, 95% confidence interval: 2.76, 8.91) than for MSS tumors (odds ratio = 2.33, 95% confidence interval: 1.66, 3.28) (P-heterogeneity = 0.02). Obesity is associated with most subtypes of endometrial cancer, and further studies are warranted to elucidate the biological mechanisms underlying the stronger risk for the MSI subtype with a high body mass index.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".