Prevalence of Peri-implant Diseases in Patients with Osteoporosis: a Systematic Review
Bibliographic record
Abstract
Objectives: This systematic review aimed to determine the prevalence of peri-implant diseases in patients affected by osteoporosis rehabilitated with dental implants. Material and Methods: An electronic search was conducted in the MEDLINE (PubMed), EMBASE, Scopus, and Web of Science databases up to April 2025, complemented by a manual search of the reference lists from the full-text studies. The search included observational studies that identified peri-implantitis and/or peri-implant mucositis in patients affected by osteoporosis rehabilitated with dental implants. The risk of bias was assessed using the Newcastle-Ottawa tool. Results: A total of 10, from 321, articles were included, and the reported evaluation periods after implant placement ranged from 3 to 11 years. Regardless of age, sex, number of implants, implant location, or duration of oral bisphosphonate use, all studies investigating the presence of mucositis and/or peri-implantitis reported no higher prevalence compared to systemically healthy patients. The prevalence of peri-implantitis was found to be 22%, and peri-implant mucositis 20%, in patients affected by osteoporosis. Conclusions: Within the limits of this systematic review, it is concluded that osteoporosis does not increase the prevalence of peri-implant diseases or dental implant failure.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 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) | 0.003 | 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".