Glycaemic index, glycaemic load and ovarian cancer risk: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: There is some evidence that plasma insulin levels might influence ovarian cancer risk. Glycaemic index (GI) and glycaemic load (GL) are measures that allow the carbohydrate content of individual foods to be classified according to their postprandial glycaemic effects and hence their effects on circulating insulin levels. Therefore, we examined ovarian cancer risk in association with GI and GL, and intake of dietary carbohydrate and sugar. METHODS: The study was conducted in a prospective cohort of 49 613 Canadian women enrolled in the National Breast Screening Study (NBSS) who completed a self-administered food-frequency questionnaire (FFQ) between 1980 and 1985. Linkages to national mortality and cancer databases yielded data on deaths and cancer incidence, with follow-up ending between 1998 and 2000. Data from the FFQ were used to estimate overall GI and GL, and Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between energy-adjusted quartile levels of GL, overall GI, total carbohydrates, total sugar and ovarian cancer risk. RESULTS: During a mean 16.4 years of follow-up, we observed 264 incident ovarian cancer cases. GI and total carbohydrate and sugar intakes were not associated with ovarian cancer risk in the total cohort. GL was positively associated with a 72% increase in risk of ovarian cancer (HR=1.72, 95% CI=1.13-2.62, Ptrend=0.01) and the magnitude of the association was slightly greater among postmenopausal (HR=1.89, 95% CI=0.98-3.65, Ptrend=0.03) than among premenopausal women (HR=1.64, 95% CI=0.95-2.88, Ptrend=0.07). CONCLUSIONS: Our data suggest that consumption of diets with high GL values may be associated with increased risk of ovarian 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".