The relationship between lead and breast cancer: A systematic review
Bibliographic record
Abstract
Introduction: There is a possibility of more accumulation of environmental pollutants due to lower levels of iron in the body of women. Different results have been reported on the association of lead with breast cancer, therefore, this systematic review study was performed with aim to determine the relationship between lead and breast cancer. Methods: In this review study, to find the related articles, international databases (Web of Science, Embase, Scopus, Google Scholar and PubMed) and national (Irandoc, Magiran, SID and IranMedex) were searched with different keyword combinations related to "Lead" And "Breast Cancer" obtained from MESH in 2000 to 2022. Selection of articles was based on inclusion and exclusion criteria and articles' quality was assessed using the Newcastle Ottawa scale. Results: Out of 4057 articles reviewed, 13 articles had good quality. All studies were in English and were observational. According to most studies, serum lead levels were not associated with breast cancer, but lead levels in bone and breast tissue were also associated with exposure to lead as an environmental contaminant with breast cancer. Conclusion: Different ways of exposure to lead have led to different results and it cannot be certainty said that exposure to lead causes breast cancer, while this relationship also depends on the method of measuring lead. In this regard, long-term studies with accurate measurement of environmental lead and body tissues, especially for women at higher risk are recommended.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".