High glycemic index and glycemic load are associated with moderately increased cancer risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it