Success Rate of Dental Implants in Patients with Autoimmune Disorders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
A bstract Aim: Autoimmune disorders may affect bone metabolism and tissue healing, potentially influencing dental implant outcomes. This systematic review and meta-analysis aimed to assess implant survival and success in patients with autoimmune diseases, with particular focus on marginal bone loss (MBL) and bleeding on probing (BOP). Materials and Methods: A comprehensive search of seven databases (PubMed, MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and CINAHL) was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Fourteen observational studies including 2657 participants were included. Risk of bias was assessed using ROBINS-I and Newcastle–Ottawa scale. Meta-analyses were performed using a random-effects model. Results: Implant survival rates ranged from 76.4% to 100% across autoimmune conditions. No significant differences in MBL were observed for patients with type 1 diabetes or Sjögren’s syndrome compared to controls, while rheumatoid arthritis (RA) patients showed significantly increased MBL [MD = 1.60 mm; 95% confidence intervals (CI) = 1.13–2.07; P < 0.00001]. RA patients also had lower odds of BOP (OR = 0.13; 95% CI = 0.05–0.36; P < 0.0001). Overall, autoimmune conditions were associated with reduced BOP (OR = 0.24; 95% CI = 0.06–0.91; P = 0.04). Conclusion: Dental implants demonstrate favorable survival in autoimmune patients, although RA and diabetes may increase MBL risk. Tailored treatment planning and follow-up are essential. Further high-quality studies are needed to confirm disease-specific risks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".