Association Between Recurrent Aphthous Ulcers and Oral Biodiversity: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Objective This study aimed to investigate the relationship between recurrent aphthous ulcers (RAU) and dysbiosis of the oral microbiota. Methods A systematic search was conducted across several databases, including the Cochrane Library, PubMed, Web of Science Core Collection, Embase, Scopus, CBM, CNKI, WanFang, and VIP, to identify relevant studies from the inception of the library until September 20, 2024. All included studies were evaluated for quality using the Newcastle–Ottawa scale. These studies assessed the diversity and abundance of oral microorganisms in patients with RAU in comparison to healthy individuals. Results were synthesized via random‐effects meta‐analysis ( I 2 statistic for heterogeneity). Mean differences (MD) and 95% confidence intervals (CI) were applied to evaluate the pooled effects of outcomes. The systematic review was registered in PROSPERO (CRD42024615516). Results A total of 10 studies involving 343 patients and 348 controls were included in this study, 7 of these were utilized for meta‐analysis to conduct a quantitative assessment. The meta‐analysis showed that in terms of alpha diversity, saliva samples collected from patients with RAU exhibited a significantly lower Chao 1 index of alpha diversity (MD = –41.22, 95% CI –64.34 to –18.09, p < 0.01), and that patients with ulcerated sites in the oral mucosa demonstrated a higher Chao 1 index when compared to healthy sites within the oral mucosa (MD = 27.48, 95% CI 2.98 to 51.97, p = 0.03). Conclusions Significant differences in the Chao 1 index between patients and healthy controls, along with variations in beta diversity and the relative abundance of taxa, indicate that microbial dysbiosis may play a crucial role in the development of RAU.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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.
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".