Risk of breast cancer in people with periodontal disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Periodontal disease (PD) has been associated with the incidence of chronic systemic diseases, including breast cancer (BC). However, studies on their association have shown inconsistent results. Objective: To evaluate the risk of BC in people with PD. Methods: A systematic review and meta-analysis following the PRISMA 2020 guidelines in Scopus, PubMed, ScienceDirect, EBSCO, Wiley Online Library, and Google Scholar was performed. Any observational study evaluating BC risk in people with and without PD was included. Study quality assessment was conducted using the Newcastle-Ottawa Scale. Fixed- or random-effects model meta-analyses were used and the results were reported as relative risks (RR) and 95% confidence intervals (CI). All statistical analyses were performed using Stata version 17.0 software. Results: Fifteen observational studies involving 816,219 female participants were included. There was a 22% increased risk of BC in people with PD (RR = 1.22; 95% CI = 1.10-1.35; p = 0.0001; I2 = 89.80%). Subgroup analysis showed consistent and significant results when stratified by sample size and follow-up period. This meta-analysis was robust based on sensitivity analysis; however, it should be interpreted with caution due to its high heterogeneity. Conclusions: The risk of BC is increased in people with PD. Future studies are needed to evaluate the effect of periodontal treatment on reducing the risk of BC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".