Implant‐prosthetic treatment in patients with oral lichen planus: A systematic review
Bibliographic record
Abstract
AIMS: This review is aiming on identifying the ideal implant-prosthetic treatment design in patients with OLP. METHODS AND RESULTS: A systematic review was conducted using four electronic databases; Medline (PubMed), Cochrane library, DOAJ and SCOPUS, following the PRISMA statement recommendations to answer the PICO question: "which implant-prosthetic treatment design is most useful to ensure implant survival in OLP patients?". The study was pre-registered in PROSPERO (CRD 42020220102). Included articles quality was assessed using the "Newcastle-Ottawa scale" and the JBI critical appraisal tool for case series. No article was found specifically designed to analyze the prosthetic influence on implant survival in OLP patients. Despite, information about implant-prostheses in studies designed with other goals was compiled. Eight articles that involved 141 patients and 341 implants were finally selected. The weighted mean follow-up was 38 months and the weighted mean survival of the implants 98.9%. No statistical differences were observed between cemented or screw retained prostheses and the materials employed or the technology to manufacture the prostheses. CONCLUSION: The influence of prosthetic design on implant survival in OLP patients is still poorly understood, but important clinical recommendations can be drawn. The strength of evidence was grade 3b (CEBM) or low (GRADE).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".