Dietary glycemic index, glycemic load, and the risk of endometrial cancer
Bibliographic record
Abstract
Carbohydrates and the dietary glycemic index (GI) influence insulin secretion and insulin-like growth factors, and may exert relevant effects on obesity and diabetes, both of which are important risk factors for endometrial cancer. We studied the association between dietary GI and glycemic load (GL) and endometrial cancer using data from an Italian case-control study. This included 454 women with histologically confirmed endometrial cancer and 908 controls admitted to the same hospitals for acute, non-neoplastic conditions. Multivariate odds ratios were obtained after allowance for major potential confounding factors, including noncarbohydrate energy intake. We updated a meta-analysis on this issue, including a recent US cohort study, which contributed about a quarter of all cases, besides our case-control study. In the case-control study, the odds ratios of endometrial cancer for the highest versus the lowest quintile were 1.03 [95% confidence interval (CI): 0.67-1.58] for GI and 1.01 (95% CI: 0.64-1.61) for GL. No heterogeneity was found across the strata of diabetes and other selected covariates. The summary risk estimate of endometrial cancer for the highest versus the lowest GI level, obtained from the meta-analysis, was 1.09 (95% CI: 0.92-1.29). The corresponding risk estimate for GL was 1.19 (95% CI: 1.06-1.34). The case-control study showed no association between dietary GI and GL and the risk of endometrial cancer overall and in the strata of relevant covariates, whereas the meta-analysis supported an increased risk for high GL, but not GI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".