Periodontitis As A Risk Factor For Stroke: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Nathalia Carolina Fernandes Fagundes,1,2 Anna Paula Costa Ponte Sousa Carvalho Almeida,1 Kelly Fernanda Barbosa Vilhena,1 Marcela Baraúna Magno,3 Lucianne Cople Maia,3 Rafael Rodrigues Lima1 1Laboratory of Functional and Structural Biology, Institute of Biological Sciences, Universidade Federal do Pará, Belém-Pará, Brazil; 2School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada; 3Department of Pediatric Dentistry and Orthodontics, School of Dentistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilCorrespondence: Rafael Rodrigues LimaLaboratory of Structural and Functional Biology, Institute of Biological Sciences, Federal University of Pará, Rua Augusto Corrêa nº 1, Guamá, Belém 66075-900, PA, BrazilEmail rafalima@ufpa.brAbstract: This systematic review and meta-analysis investigate the association between periodontitis and stroke. This review followed the methods established by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Searches were conducted in five databases and two sources of grey literature. After the selection of the articles, a risk of bias evaluation was performed. Three meta-analyzes were performed: Assessing the overall association between stroke and periodontitis in case–control studies; Ischemic stroke and periodontitis in case–control studies; The association between stroke and periodontitis in cohort studies. Heterogeneity was assessed using the I2 index and the odds ratio was also calculated (p < 0.05). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) was applied to evaluate the level of evidence. 2193 potentially relevant studies were identified, with 10 studies included in qualitative and quantitative analysis. All the articles were considered with low risk of bias and a low level of certainty. The results demonstrated a positive association between both disorders and increased risk for stroke among cohort studies (RR 1.88 [1.55, 2.29], p<0.00001, I2=0%) and for ischemic stroke events in case–control studies (RR 2.72 [2.00, 3.71], p<0.00001, I2= 4%). Periodontitis may represent a risk factor for stroke, especially in ischemic events. However, new studies with a robust design are necessary for a reliable conclusion.Keywords: central nervous system, cerebrovascular disorders, stroke, periodontitis, periodontal attachment loss
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".