Systematic review and meta-analysis of the association of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with periodontitis
Bibliographic record
Abstract
INTRODUCTION: Recent evidence suggests the relationship between periodontitis and systemic inflammation, which complete blood count can assess (CBC)-derived biomarkers such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). We conducted this meta-analysis to evaluate the levels of NLR and PLR of patients with periodontitis compared to those of healthy controls. METHODS: Web of Science, PubMed, ProQuest, Scopus, and Open Grey were searched for studies published before October 20, 2024, without any limitation on date and language; then, using the random-effects model, we reported a standardized mean difference (SMD) with a 95% confidence interval (CI). In order to assess the quality of publications, we used the Newcastle-Ottawa scale (NOS). Our study was registered in PROSPERO (CRD42023475214). RESULTS: Overall, 11 articles were included in the analysis. We found that patients with periodontitis had elevated levels of NLR compared to healthy controls (SMD = 0.30, 95% CI 0.08-0.52, p = 0.007) In the subgroup analysis according to race, patients with periodontitis had elevated levels of NLR compared to healthy controls in among East Asian patients (SMD = 0.35, 95% CI 0.15-0.55, p = 0.001), but not among Turkish (SMD = 0.15, 95% CI - 0.30-0.61, p = 0.50) and Indian (SMD = 0.38, 95% CI - 0.17-0.94, p = 0.18) patients. In addition, PLR level was not different among patients with periodontitis and healthy controls (SMD = 0.06, 95% CI - 0.71-0.83, p = 0.87). CONCLUSIONS: The findings of our investigation, which indicate higher NLR levels in periodontitis patients, show that immune dysregulation plays a role in the etiology of the disease.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.007 | 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, 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".