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 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 | 0.027 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".