Glycemic index, glycemic load and risk of prostate cancer
Bibliographic record
Abstract
Dietary carbohydrates have different glycemic and insulinemic potentials depending on type (glycemic index, GI) and amount (glycemic load, GL) of carbohydrate consumed or both. Insulin in turn has been implicated as a risk factor for several cancers, including that of the prostate. We assessed the relationship of GI and GL with prostate cancer risk in a multicenter case-control study. Cases and controls were recruited between 1991 and 2002 in the network of major teaching and general hospitals in 4 Italian areas. Cases were 1,204 men (age range 46-74 years) admitted for incident, histologically confirmed prostate cancer. Controls were 1,352 men (age range 46-74 years) admitted for acute, nonmalignant conditions unrelated to long-term modifications of diet. ORs of prostate cancer and the corresponding 95% CIs were derived using unconditional multiple logistic regression, including terms for age, study center, education, family history of prostate cancer, smoking, body mass index, physical activity, alcohol consumption, intake of energy, fiber and lycopenes. Compared to the lowest quintile of GI, the ORs were 1.23, 1.24, 1.47 and 1.57 for subsequent levels of GI. The corresponding values for GL were 0.91, 1.00, 1.20 and 1.41. No heterogeneity was found among strata of selected covariates. We found direct relations between dietary GI and GL and prostate cancer risk. Correcting for potential confounding factors did not substantially modify these associations.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".