A Dose-response Meta-analysis of Egg Intake and Breast Cancer Risk
Bibliographic record
Abstract
Abstract: Aim Eggs are one of the most nutritious foods in nature, but there is no unified conclusion about the association between egg intake and breast cancer risk. Methods The PubMed and Web of Science databases for the literature on egg intake and breast cancer risk were searched for papers published during the last 10 years. These were then filtered according to the inclusion and exclusion criteria. Stata16.0 software was applied to perform a metaanalysis, the generalized least squares method and constrained cubic spline model were used to assess the dose-response trends between egg intake and breast cancer risk. Results A total of 9 articles were included: 6 case control studies and 3 cohort studies. The Newcastle-Ottawa scale (NOS) values of the included articles were all ≥ 6 points. The pooled relative risks (RR) of egg intake and breast cancer risk was 0.91 (95% CI: 0.69-1.19). The dose-response analysis showed a linear trend for egg intake and breast cancer risk (P = 0.689). With every 10 g/day increase in egg intake, the incidence of breast cancer increased by 2% (RR = 1.02, 95% CI: 0.99-1.05). However, these results were not statistically significant. Conclusion This meta-analysis found no significant association between egg intake and breast cancer.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".