Fixed Implant‐Retained Rehabilitation of the Edentulous Maxilla: 11‐Year Results of a Prospective Study
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to assess long-term survival and success rates of implants in the edentulous maxilla restored with an implant-supported fixed prosthesis. MATERIALS AND METHODS: Seventeen edentulous patients received six to eight implants and implant-supported fixed prostheses by one surgeon. Yearly recalls were conducted by two examiners over a period of 11 years. Survival and success rates (biological complications) were determined; marginal bone loss was examined radiographically. Furthermore, microbiological tests as well as test for interleukin-1 composite genotype were assessed and potential risk factors were evaluated. RESULTS: After a mean time of 11.26 years, 15 patients of 17 could be reexamined. Out of 94 implants, three were lost in one patient. Mean marginal bone loss reached 0.88 mm, two patients (at seven implants) showed bone loss of ≥3.2 mm. Survival rate of implants reached 96.8%. Success rates on implant level hit 92.6% according to the criteria of Albrektsson and colleagues and 83.0% in accordance with Karoussis and colleagues. One prosthesis had to be renewed. CONCLUSION: Within the limitation of this study, restoration of the edentulous maxilla with an implant-supported fixed prosthesis represents an effective tool for rehabilitation over a period of 11 years.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".