Association between oral health and atrial fibrillation: A systematic review
Bibliographic record
Abstract
Background and objective: Poor oral health increases systemic inflammation, which has complex pathophysiologic links with atrial fibrillation (AF). The aim of this comprehensive systematic review was to investigate the association between oral health and AF in terms of new-onset AF and AF recurrence. Methods: After PROSPERO protocol was registered, PubMed, Scopus, and Cochrane Database of Systematic Reviews were standardly searched from database inception to February 2021. The included studies were assessed for quality and risk of bias using the modified Newcastle-Ottawa scale. The indicators of poorer oral health were the presence of periodontitis, lower frequency of dental scaling, lower frequency of toothbrushing, and lower number of missing teeth. Results: We initially identified 424 studies; however, only 5 studies met the inclusion criteria. The included studies comprised 3 nationwide population-based retrospective cohort studies, 1 large prospective cohort study, and 1 case-control study that reported the association between oral health and AF. These studies demonstrated that poor oral health was associated with new-onset AF, and may promote AF recurrence and progression. Moreover, AF patients with poorer oral health may have a higher risk of arrhythmias and major adverse cardiovascular events during long-term follow-up. Conclusion: Improved oral health potentially reduces new-onset AF. Periodontitis prevention, regular dental visits for professional dental scaling, and frequent tooth brushing, are oral health care interventions that contribute to AF protection. Therefore, promoting oral health should be integrated as a part of AF primary prevention.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".