Is oral lichen planus a risk factor for peri-implant diseases? A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: To evaluate whether oral lichen planus (OLP) is a risk factor for peri-implant diseases (PIDs) with a systematic review and meta-analysis.Methods: Five electronic databases including Medline, Embase, Web of Science, the Cochrane Library and Scopus were searched. The included studies are observational human studies written in English. The population of interest included those with/without OLP who received dental implant treatment. The follow-up time after implantation was from one month to 20 years. The quality of the included articles regarding risk of bias and methodology were assessed with the Newcastle-Ottawa Scale or the Agency for Healthcare Research and Quality. The data involving exposure (OLP), primary outcomes (implants having PIDs) and secondary outcomes (probing depth/PD, bleeding on probing/BOP and bone loss/BL) and potential confounders were extracted. Heterogeneity was assessed by I² test. Dichotomous data are expressed as the risk ratio (RR) and 95% confidence interval (CI) which were calculated with a fixed effect model.Results: Of the 66 articles, two studies were enrolled and evaluated as high quality, which totally contained 68 participants receiving 222 (OLP vs. non-OLP, 112 vs. 110) implants with 12 to 120-month follow-up time. Proportions of implants with PIDs between OLP and non-OLP groups were as follows: 19.6% (22/112) vs. 22.7% (25/110) for PIM and 17.0% (19/112) vs. 10.9% (12/110) for PI. The meta-analysis revealed no recognizable difference in number of implants with PIDs (PI: RR = 1.49, 95% CI 0.77-2.90, P = 0.24; PIM:RR = 0.88, 95% CI 0.53 -1.46, P = 0.61; PIDs: RR = 1.08, 95% CI 0.75 -1.55, P = 0.68) or BOP (RR = 0.90, 95% CI: 0.70-1.15, P = 0.40) between OLP and non-OLP groups.Conclusions: Available articles regarding the effects of OLP on PIDs remains very limited. Existing evidence does not support OLP as a suspected risk factor for PIDs. Large-scale prospective trials are required to validate the findings.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".