Oral Lichen Planus and Dental Implants – A Retrospective Study
Bibliographic record
Abstract
OBJECTIVES: To examine whether oral lichen planus (OLP) affects the success rate of dental implants and if the manifestations of OLP are altered by implant-borne prostheses. MATERIALS AND METHODS: OLP patients, treated in the oral medicine department, with (the study group) and without (control group) dental implants were included. Pocket depth, mobility, bleeding on probing, erythema, pain and radiolucency around the implants, as well as clinical findings and OLP symptoms were recorded. Follow-up ranged from 12-24 months. Ordinal variables and visual analog scale score were compared using the Mann-Whitney test. The significance of the trend within each of the groups was assed using the Friedman test. Categorical variables were compared using Pearson chi-squared test and Fisher's exact test. RESULTS: Fourteen patients in the study group with 1-15 implants per patient and 15 in the control group were included. No implant failures were recorded. Comparison between the clinical manifestations of OLP in both groups did not reveal any significant differences. CONCLUSIONS: Success of implant rehabilitation among treated OLP patients does not seem to be different from the success rate in the general population. Nor does implant placement influence the disease manifestations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".