Survival and Success of Dental Implants in Patients with Autoimmune Diseases: a Systematic Review
Bibliographic record
Abstract
Objectives: The purpose of this systematic review is to disclose the impact of autoimmune diseases and their medical treatment on dental implant survival and success. Material and Methods: , 2021. Any clinical study on patients with an autoimmune disease in whom implant therapy was performed was eligible. The quality of included studies was assessed using the Newcastle-Ottawa Scale. For each autoimmune disease group, data synthesis was divided into three groups: 1) overall results of the autoimmune disease, 2) overall results of corresponding control groups and 3) overall results of the autoimmune disease with a concomitant autoimmune disease (a subgroup of group 1). Descriptive statistics were used. Results: Of 4,865 identified articles, 67 could be included and mainly comprising case reports and retrospective studies with an overall low quality. Implant survival rate was 50 to 100% on patient and implant level after a weighted mean follow-up of 17.7 to 68.1 months. Implant success was sporadically reported. Data on immunosuppressive medication were too heterogeneously reported to allow detailed analysis. Conclusions: Overall, a high implant survival rate was reported in patients with autoimmune diseases. However, the identified studies were characterized by a low quality. No conclusions could be made regarding implant success and the effect of immunosuppressants due to heterogeneous reporting.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| 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".