Atherosclerosis and Chronic Apical Periodontitis: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background. Atherosclerosis is a chronic and progressive condition of the arteries, characterized by the thickening and hardening of their walls due to the formation of atherosclerotic plaques. Low-grade inflammation is implicated in the pathogeny of atherosclerosis. Chronic apical periodontitis (CAP), the chronic inflammation around the root apex of infected teeth, is associated with a low-grade inflammatory state, so a connection between atherosclerosis and CAP have been suggested. The aim of this study was to conduct a systematic review with meta-analysis to answer the following PICO question: In adult patients, does the presence or absence of atherosclerosis affect the prevalence of CAP? Methods. The PRISMA guidelines were followed to carry out this systematic review. A bibliographic search was performed in PubMed-MEDLINE, Embase, and Scielo. Inclusion criteria selected the studies presenting data on the prevalence of CAP in patients diagnosed with atherosclerosis and control patients. The statistical analysis was carried out using RevMan software. Study characteristics and risk ratios with 95% confidence intervals (CIs) were extracted. Random-effects meta-analyses were performed. Risk of bias was assessed using the Newcastle-Ottawa scale, adapted for cross-sectional studies. To estimate variance and heterogeneity between trials, the Higgins I2 test was used. The quality of the evidence was evaluated using GRADE. Results. The search strategy recovered 102 articles, and only five met the inclusion criteria. Meta-analysis showed and overall OR = 2.94 (95% CI = 1.83 – 4.74; p < 0.01) for the prevalence of CAP among patients with atherosclerosis. The overall risk of bias was moderate. The quality of the evidence showed a low level of certainty. Conclusions. Patients with atherosclerosis are almost three times more likely to have CAP. This finding supports the hypothesis that chronic inflammatory processes in the oral cavity could significantly impact cardiovascular health, emphasizing the importance of an integrated approach to oral and systemic health care. This result should be translated to daily clinical practice: the healthcare community should be aware of this association and suspect atherosclerotic pathology in patients who show a high prevalence of CAP. Likewise, patients with atherosclerosis should be monitored in the dental clinic for CAP.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".