Associations between serum uric acid and breast cancer incidence: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Serum uric acid (SUA) may be involved in the development of cancer by inhibiting oxidative stress, but its relationship with breast cancer remains unclear. MATERIALS AND METHODS: The PubMed, Embase, and Web of Science databases were searched systematically for studies on SUA levels in women with breast cancer and the effect of SUA levels on the risk of breast cancer. The Newcastle‒Ottawa Quality Assessment Scale (NOS) was used to assess the quality of all relevant studies included. RESULTS: A total of 19 studies were included, including 75,827 women with breast cancer and 508,528 healthy controls. A meta-analysis found that SUA levels were negatively correlated with breast cancer risk in women (HR = 0.94, 95% CI: 0.89 - 0.99, p = 0.003). SUA levels in female breast cancer patients were not significantly different from those in healthy controls (SMD = 0.49, 95% CI = -0.09 - 1.08, p = 0.10), while SUA levels were increased in female breast cancer patients in articles published after 2010, SUA concentration detected by spectrophotometry, and non-Asian populations, regardless of menopausal state and treatment state. CONCLUSION: High levels of SUA may reduce the risk of breast cancer in women, suggesting that SUA was a protective factor in women.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".