Association between periodontitis and breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Breast cancer (BC) stands out as the most incident type of cancer in women worldwide. Periodontitis is a multifactorial chronic inflammatory disease that involves a complex interaction between pathogenic stimuli and host response, characterized by progressive destruction of the tooth support apparatus. Systemic dissemination of infectious agents and inflammatory mediators of periodontitis can lead to a chronic systemic inflammatory condition, which may be associated with the pathogenesis of distal inflammatory processes such as cancer. The aim of this study is to systematically review the literature to assess the association between periodontitis and BC. The research protocol was structured according to the recommendations of the Cochrane Collaboration. Studies were searched in MEDLINE (via PubMed), EMBASE, LILACS, Web of Science, Google Scholar, and Open Gray (DANS) electronic databases up to June 2022. Longitudinal and case-control studies were included. Meta-analyses determined risk estimates (relative risk; RR and 95% confidence interval; CI). Two independent reviewers carried out the selection, data extraction, risk of bias assessment (Newcastle-Ottawa Scale) and quality of evidence (GRADE). A total of seventeen studies were included. Of these, 9 studies are prospective cohorts, 3 retrospective cohorts and 5 case-controls. The meta-analysis showed that women with periodontitis have an 18% greater risk of BC occurrence than those without or with milder forms of periodontitis (RR 1.18; 95% CI 1.05 to 1.33). Subgroup analyzes and metaregression showed that estimates from studies conducted in middle/lower-middle income countries and with diagnosis of periodontitis based on clinical examination were significantly higher than those in high/upper-middle income countries and diagnosed by self-report (p<0.05). Our findings confirmed that periodontitis is associated with BC and that the origin of the sample and the diagnosis of periodontitis partly explain the heterogeneity found. A better understanding of this association may have important clinical and public health implications, given the possibility that prevention and treatment of periodontitis may minimize the onset or progression 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".