Mucosal Manifestations in the Edentulous Maxilla with Implant Supported Prostheses: Clinical Results from a Well‐Maintained Patient Cohort
Bibliographic record
Abstract
BACKGROUND: Prostheses in the edentulous maxilla affect the mucosa. PURPOSE: To evaluate mucosal alterations with implant supported fixed prostheses (FDP) and overdentures (IOD). MATERIAL AND METHODS: Patients receiving prostheses during a time period of 10 years were recruited. Maxillary mucosal conditions in relation to FDPs, IODs were analyzed. Peri-implant parameters were measured and the Oral Health Impact Profile (OHIP) was administered. RESULTS: One hundred seven patients wearing 74 IODs and 33 FDPs were identified with a total of 519 implants, the mean observation time was 6.5 ± 2.7. Cumulative implant survival was 93%. Erythema and hyperplastic tissue were identified in 71% of the IOD wearers, but were mostly absent with FDPs. The peri-implant parameters demonstrated healthy peri-implant mucosa. Medication and smoking had no effect on mucosal alteration (OR = 1.065 and 1.568). The average OHIP value was 3.73 ± 4.12. A lower value (p < 0.0048) was found for FDPs and one type of IOD. CONCLUSIONS: A rigorous maintenance program did not prevent IOD mucosal alterations in IOD wearers, but the health of the peri-implant mucosa was maintained and was comparable for all types of prostheses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".