Assessment of the association between periodontal disease and total cancer incidence and mortality: a meta-analysis
Bibliographic record
Abstract
Background: Periodontal disease (PD) is a chronic inflammatory disease that leads to alveolar bone resorption and tooth loss. Many studies have reported the association between periodontal disease and various cancers including oral cancer, lung cancer, breast cancer and so on. However, there is still no specialized meta-analysis that assesses the association between periodontal disease and cancer incidence and mortality in-deepth. Thus, we conducted this meta-analysis. Methods: This meta-analysis was registered with PROSPERO: CRD42020183497. We searched five online databases for observational studies about the association between periodontal disease and breast, prostate, lung and bronchial, colorectal, and total cancers by July 2020. Then we evaluated quality of the included studies by the Newcastle-Ottawa scale. Risk ratios (HRs) and their 95% confidence intervals (CIs) were pooled to evaluate the strength of the association between periodontal disease and four cancers, total cancer incidence and mortality. In addition, we analyzed heterogeneity by subgroup analysis and sensitivity analysis. Finally, we inspected publication bias by Begg's and Egger's tests. Results: = 0.718). Subgroup analyses showed that study population, study design, dental status, follow-up period, adjustment for smoking partially explained the heterogeneity between studies. The results of Begg's test and Egger's test were consistent and indicated that there is no publication bias in this study. Conclusion: In conclusion, this meta-analysis revealed a positive relationship between periodontal disease and breast cancer incidence and total cancer mortality. Further well-designed studies with specific inclusion and exclusion criteria are required to strengthen the conclusion of this meta-analysis. However, longer follow-up period, multi-center trials and even multinational studies are required to corroborate the results.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.086 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".