Reciprocal cancer risks between thyroid and breast cancer: a systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
Thyroid cancer (TC) and breast cancer (BC) are common in females, with growing evidence of their higher-than-expected co-occurrence. The purpose of this systematic review and meta-analysis was to evaluate the relationship between TC and BC and to examine the likelihood of developing BC after TC (TC1-BC2) and TC after BC (BC1-TC2). A systematic search was conducted in PubMed and Embase for articles with epidemiological evidence of TC and BC, published until 2024. For BC1-TC2 studies, subgroup analysis was performed on age at diagnosis and treatment type. The standardized incidence ratio (SIR) was used to calculate the risk of second primary malignancy. The MOOSE guidelines were followed, and the Newcastle-Ottawa scale was used to assess the quality of studies. Sixteen studies comprising 511,787 patients were included in the meta-analysis of TC1-BC2 and showed an increased risk of BC after TC (SIR = 1.4, 95% CI: 1.2-1.6, P < 0.01). Moreover, 28 studies with 2,486,870 patients were included for the BC1-TC2 meta-analysis and also demonstrated an increased risk of TC after BC (SIR = 1.5, 95% CI: 1.3-1.7, P < 0.01). The risk of TC was higher in BC patients under 50 (SIR = 1.8, 95% CI: 1.2-2.3) and in those treated with chemotherapy (SIR = 1.6, 95% CI: 1.5-1.7). Radiotherapy for BC was not linked to an increased risk of TC. Here, we demonstrated an increased risk of TC or BC as secondary malignancies. Furthermore, studies are needed to better understand this association and its implications for patient follow-up and management strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it