High glycemic index and glycemic load are associated with moderately increased cancer risk
Bibliographic record
Abstract
SCOPE: To obtain an up-to-date quantification of the association between dietary glycemic index (GI) and glycemic load (GL) and the risk of cancer. METHODS AND RESULTS: We conducted a systematic review and meta-analysis of observational studies updated to January 2015. Summary relative risks (RRs) were derived using random effects models. Seventy-five reports were evaluated in the systematic review (147,090 cases), and 72 were included in the meta-analyses by cancer site. Considering hormone-related cancers, summary RRs comparing the highest versus the lowest GI and GL intake were, respectively, 1.05 and 1.07 for breast, 1.13 and 1.17 for endometrial, 1.11 and 1.19 for ovarian, and 1.06 and 1.04 for prostate cancers. Considering digestive-tract cancers, summary RRs for GI and GL were, respectively, 1.46 and 1.25 for esophageal (squamous cell carcinoma), 1.17 and 1.10 for stomach, 1.16 (significant) and 1.10 for colorectal, 1.11 and 1.14 for liver, and 1.10 and 1.01 for pancreatic cancers. In most of these meta-analyses, significant heterogeneity among studies was observed. In subgroup analyses, case-control studies and studies from Europe tended to estimate higher RRs. CONCLUSION: High-GI and high-GL diets are related to moderately increased risk of cancer at several common sites.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".