Thyroid disease is associated with an increased risk of breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study investigated the relationship between thyroid diseases and the risk of breast cancer (BC). Clarifying this issue can help medical staff perform of early prevention, diagnosis and treatment for breast cancer patients. METHODS: The meta-analysis combined data from cohort studies and case-control to obtain a comprehensive result of the relationship between thyroid diseases and risk of BC. We comprehensively searched PubMed, EMbase, Web of Science, and the Cochrane Library. The search period was from the establishment of the databases to August 2020. Literature was collected and screened individually by two reviewers. There was English language restriction on the search and unpublished literature was excluded. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the selected studies prior to data extraction. The data collected included country, author, year of publication, research type, and number of cases. In cases where the data and study heterogeneity permitted, meta-analyses were performed, and odd ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated. Data were analyzed using the STATA 15.1 software. RESULTS: A total of 21 articles were included in this study. Hyperthyroidism, thyroid cancer, thyroglobulin antibody (TGAb) levels, and thyroid microsomal antibody (TPOAb) levels were all significantly associated with an increased risk of BC, while hypothyroidism was associated with a reduced risk of BC. CONCLUSIONS: This study demonstrated that hyperthyroidism, autoimmune thyroiditis (AITD), and thyroid cancer are significantly associated with an increased risk of BC, while hypothyroidism is associated with a reduced risk of BC.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".