Associations of dietary carbohydrates, glycaemic index and glycaemic load with risk of bladder cancer: a case–control study
Bibliographic record
Abstract
Carbohydrate foods with high glycaemic index (GI) and load (GL) may negatively influence cancer risk. We studied the association of dietary carbohydrates, GI, GL, intake of bread and pasta with risk of bladder cancer using data from an Italian case-control study. The study included 578 men and women with histologically confirmed bladder cancer and 608 controls admitted to the same hospitals as cases for acute, non-neoplastic conditions. OR were estimated by logistic regression models after allowance for relevant confounding factors. OR of bladder cancer for the highest v. the lowest quantile of intake were 1·52 (95 % CI 0·85, 2·69) for available carbohydrates, 1·18 (95 % CI 0·83, 1·67) for GI, 1·96 (95 % CI 1·16, 3·31, P trend<0·01) for GL, 1·58 (95 % CI 1·09, 2·29, P trend=0·03) for pasta and 1·92 (95 % CI 1·28, 2·86, P trend<0·01) for bread. OR for regular consumption of legumes and whole-grain products were 0·78 (95 % CI 0·60, 1·00) and 0·82 (95 % CI 0·63, 1·08), respectively. No heterogeneity in risks emerged across strata of sex. This case-control study showed that bladder cancer risk was directly associated with high dietary GL and with consumption of high quantity of refined carbohydrate foods, particularly bread. These associations were apparently stronger in subjects with low vegetable consumption.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".