Impact of cerebrovascular stroke on inflammatory periodontal indices: a systematic review with meta-analysis and trial sequential analysis of case-control studies
Bibliographic record
Abstract
Introduction: Cerebrovascular stroke, a leading cause of global morbidity and mortality, is influenced by several modifiable risk factors such as hypertension, diabetes, and smoking. Emerging evidence highlights the significant role of inflammation in stroke pathogenesis, with conditions like periodontitis potentially exacerbating this risk. The aim of this systematic review was to identify and quantify the real impact of periodontal disease in individuals with cerebral stroke through the analysis of inflammatory periodontal indices. Methods: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of case-control studies assessing periodontal indices in stroke patients. Data sources included PubMed, Scopus, and Cochrane Library, with searches extended to grey literature. The review protocol was registered on PROSPERO (CRD42024529767). Studies were evaluated using the Newcastle-Ottawa Scale to assess risk of bias, and meta-analyses were conducted using Review Manager 5.4 and TSA software. Results: The review included seven case-control studies, comprising 723 stroke patients and 787 controls. Meta-analyses revealed significant differences between stroke and control groups in clinical attachment loss [MD 1.04 mm, 95% CI (0.54, 1.54)], probing pocket depth [MD 0.68 mm, 95% CI (0.31, 10.6)], and radiological bone loss (MD 2.15 mm, 95% CI [-1.58, 5.89]. These findings indicate that stroke patients exhibit worse periodontal health compared to controls, supporting a potential link between periodontal inflammation and stroke. Conclusion: This study confirms the significant impact of periodontal disease on stroke patients and highlights the importance of oral health in preventing adverse cerebrovascular events. Systematic Review Registration: PROSPERO, identifier (CRD42024529767).
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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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.046 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 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".