Association between oral health and atrial fibrillation: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it