Prior tonsillectomy and subsequent risk of breast cancer in females: Systematic review and meta-analysis.
Bibliographic record
Abstract
10568 Background: Exposure to recurrent infections in childhood was linked to an increased risk of cancer in adulthood. There is also evidence that a history of tonsillectomy, a procedure often performed in children with recurrent infections, is linked to an increased risk of leukemia, and Hodgkin lymphoma. Tonsillectomy could be directly associated with cancer risk or it could be a proxy for another risk factor such as recurrent infections and chronic inflammation. Nevertheless, the role of recurrent childhood infections and tonsillectomy on the one hand, and the risk of breast cancer (BC) in adulthood remain understudied. Our study aims to verify whether a history of tonsillectomy increases the risk of BC in women. Methods: A systematic review was conducted using PubMed, Google Scholar, Scopus, Embase and Web of Science databases from inception through November 2020 to identify the studies which explored the association between history of tonsillectomy and BC in females. The Newcastle Ottawa Scale was used to assess the quality of included studies. Odds ratio (OR) was used to measure effect size. The Random/Fixed effects model was applied to synthesize the associations between tonsillectomy and BC risk based on heterogeneity. Heterogeneity was assessed using the I-squared statistic. A forest plot was generated, and publication bias was assessed. The leave-one-out sensitivity analysis was performed to check if results were driven by a single study. Results: Seven studies with a total of 7259 patients were included in our analysis; out of them, 2200 patients were diagnosed with BC. Patients with a history of tonsillectomy (n = 2843) showed higher subsequent risk of developing BC (OR = 1.252; 95% CI = 1.115 - 1.406; P < 0.001; I2 = 9%) as compared to patients without a history of tonsillectomy (n = 4416). Using the leave-one-out sensitivity analysis to iteratively remove one study at a time, we confirmed that no single study had a substantial influence on the overall effect size. Conclusions: Our study supports and confirms the evidence that a history of tonsillectomy is associated with an increased risk of breast cancer. These findings are also an argument in support of the hypothesis that recurrent childhood infections are linked with adulthood 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".