Association of Chronic Periodontitis with Hemorrhagic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Periodontitis is a chronic, multifactorial inflammatory condition linked to dysbiotic plaque biofilms and characterized by the gradual destruction of the structures supporting the teeth owing to compromised immune system function. Hemorrhagic stroke, which primarily occurs within the brain tissue or in the subarachnoid space as a blood leak of ruptured vessels, is a sudden neurological impairment caused by vascular damage in the central nervous system, resulting in focal neurological deficits. Chronic periodontitis (CP) and hemorrhagic stroke may share common pathogenic features involving inflammation and immune system activation, prompting researchers to investigate their potential connection. The aim of the study is to systematically review the literature on the epidemiological association between CP and hemorrhagic stroke in adults. The study protocol adhered to the PRISMA 2020 guidelines, and the design followed the Cochrane methodology. A thorough literature search encompassing PubMed, Scopus, and Web of Science databases and a manual search and evaluation of gray literature was conducted. Meta-analysis was performed using Review Manager (RevMan) 5.4, with the effect size represented by the odds ratio (OR) and a 95% confidence interval (CI). Heterogeneity was assessed using the chi-squared and I 2 statistics. The selected articles, written in English without time constraints, focused on observational studies involving patients and controls and included disease diagnostic criteria. Duplicate entries were eliminated. The reliability of each study's results was evaluated using the Newcastle-Ottawa Scale and GRADE tools. Two reviewers conducted the assessments, and a third reviewer resolved any disagreements. The meta-analysis comprised four observational studies involving 1,882 individuals. It revealed that individuals diagnosed with hemorrhagic stroke were notably more likely to have concurrent CP (OR: 6.32; 95% CI: 1.35–29.49; p = 0.02) or severe CP (OR: 3.08; 95% CI: 1.56–6.06; p = 0.001) compared with healthy controls. A notable occurrence of CP was detected in patients with hemorrhagic stroke compared with controls. Health care professionals need to acknowledge the connection between the two conditions, as it allows them to provide optimal holistic care through a thorough approach to diagnosis and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".