Study of microRNAs in Gingival Crevicular Fluid as Periodontal Diseases Biomarkers: Systematic Review
Bibliographic record
Abstract
AIM: The aim of this review was to identify the microRNAs (miRNAs) present in gingival crevicular fluid (GCF) that can be used as biomarkers for the diagnosis of periodontal diseases, and to determine which of them has a higher diagnostic yield for periodontitis. METHODS: The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (reference number CRD42024544648). The Pubmed, Scopus, Cochrane Library, Embase, Web of Science, and Google Scholar databases were searched for clinical studies conducted in humans investigating periodontal diseases and miRNAs in GCF. The methodological quality of the articles was measured with the Newcastle-Ottawa Scale. RESULTS: A total of 3222 references were identified in the initial literature search, and 16 articles were finally included in the review. The design of the studies was heterogeneous, which prevented a meta-analysis of the data. Most of the studies compared miRNA expression levels between patients with periodontitis and healthy controls. The most widely researched miRNA in periodontal diseases was miR-200b-3p and miR-146a. CONCLUSIONS: the miRNAs most studied are miR-146a, miR-200b, miR-223, miR-23a, and miR-203, and all of them except miR-203 have an acceptable diagnostic plausibility for periodontitis.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".