Association between Cardiovascular Diseases and Peri-Implantitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A potential relationship between oral inflammation and cardiovascular disease has been proposed; however, the impact of cardiovascular disease on implant restoration remains unclear. This systematic review aims to assess the relationship between peri-implantitis and cardiovascular disease based on review of data obtained through observational studies. Materials and Methods: An extensive systematic literature search was performed using the PubMed/MEDLINE, Scopus, Web of Science and Cochrane Library databases. Studies published in English language up to June 2022 were conducted in accordance with PRISMA guidelines. These efforts identified 230 unique publications and, after selection, five studies were included in this meta-analysis. The Newcastle-Ottawa Scale table was used for literature quality assessment. A fixed-effect model was selected and RevMan software version 5.3 was used to identify the origin of the outcomes of the meta-analysis. Finally, results were reported through the PRISMA statement. Results: 0.05). Conclusions: Based on current evidence, we conclude that the presence of cardiovascular disease increases the incidence of peri-implantitis. Registration: PROSPERO database (CRD42022353693).
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 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".