Oral prosthetic rehabilitation in patients with Epidermolysis Bullosa Hereditaria: a systematic review
Bibliographic record
Abstract
PURPOSE: To review the oral prosthetic rehabilitation of patients with epidermolysis bullosa hereditaria (EBH) and study the level of evidence and quality of the presented available articles and establish clinical recommendations for the prosthetic management of these patients. MATERIALS AND METHODS: An electronic search was done in July 2022 in five databases following PICOTS elements. The quality of the reports was established using the modified Pierson, Bradford Hills, and Ottawa Newcastle scale. RESULTS: Data extracted from 19 case reports for protocolized analysis corresponded to 64 patients and 80 dental prostheses, with almost 9 out of 10 patients being completely edentulous. The distribution of EBH types was 84% dystrophic, 10.5% junctional, and 5.5% simplex. The difficulties encountered by the authors synthesized in this review characteristically reflected those most likely encountered in dental practice. Most rehabilitations were implant-supported prostheses (85%), followed by removable dentures (10%) and dental-supported rehabilitations (5%). Fixed full-arch implant-supported prostheses represented 76.4% of implant-supported prostheses, and this prosthetic solution reflected the highest scientific quality. CONCLUSIONS: For an individualized approach to treatment, we recommend that in cases of total edentulism, fixed full-arch implant-supported prostheses are the most appropriate because they allow the best computer-aided planning, design, manufacture, and fitting of the prosthesis in such a complex clinical context.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".