Blood Cell Count Inflammatory Markers as Prognostic Indicators of Periodontitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
(1) Background: Our study aimed to assess the association between the neutrophil to lymphocyte ratio (NLR), platelet to leukocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), red cell distribution width (RDW), and systemic immune inflammation index (SII) and periodontitis. (2) Methods: We searched PubMed, Embase, Scopus, Web of Science, and LILACS databases, identifying observational studies. The Newcastle Ottawa scale was used to evaluate the quality of the included studies. The principal summary outcome measure in our random effects meta-analysis was the mean difference (MD). (3) Results: After screening 682 search results, a total of 10 studies including 3164 subjects were selected for quantitative assessment. We found a higher mean NLR, PLR, and LMR in the periodontitis group compared to the control group (0.41 (95% CI 0.12–0.7), p = 0.006; 7.43 (95% CI 0.31–14.54), p = 0.04; 2.05 (95% CI 0.27–3.83), p = 0.024). No differences were observed for RDW. (4) Conclusions: We found an association between NLR, LMR, and PLR and periodontitis, which might be thought of as emerging blood cell count inflammatory biomarkers that could shed light on the link between periodontitis and systemic disbalances, as well as for periodontitis prognosis and grading.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".