Impact of Peri-Implant Inflammation on Metabolic Syndrome Factors: A Systematic Review
Bibliographic record
Abstract
This systematic review aims to evaluate the impact of peri-implantitis on the components of metabolic syndrome, and to provide suggestions on the management of peri-implantitis patients with metabolic disorders. A search for relevant records was performed in MEDLINE, EMBASE, and Global Health on 1st September 2023. Clinical trials, cohort studies, cross-sectional studies, and case-control studies containing comparisons of metabolic factors between patients with and without peri-implantitis were considered eligible. Study quality was assessed using the Newcastle–Ottawa scale. Out of 1158 records identified, 5 cross-sectional studies were eligible for final inclusion. Two studies reported significant differences in the lipid profile of patients with peri-implantitis, one of which reported higher total cholesterol and LDL cholesterol levels, while the other reported higher triglyceride levels. Another study reported significantly higher HbA1c levels in patients with peri-implantitis. The remaining two studies containing comparisons of BMI between patients with and without peri-implantitis indicated no significant differences. Overall, there are suggestions that peri-implantitis is associated with altered metabolic factors, including lipid profile and HbA1c level. However, there is not enough evidence to support these clinical implications due to the paucity of related literature and the low evidence level of the included studies. More investigations with stronger evidence levels are needed to narrow this gap of knowledge.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".