Analysis of technical complications and risk factors for failure of combined tooth‐implant‐supported fixed dental prostheses
Bibliographic record
Abstract
BACKGROUND: The oral rehabilitation with fixed restorations supported by the combination of teeth and dental implants has been advocated in some cases. PURPOSE: To assess the clinical outcomes of these prostheses. Fixed restorations supported by the combination of teeth and dental implants. MATERIALS AND METHODS: This retrospective study included all patients treated with combined tooth-implant-supported fixed dental prostheses (FDPs) at one specialist clinic. Abutment/prosthesis failure and technical complications were the outcomes analyzed. RESULTS: A total of 85 patients with 96 prostheses were included, with a mean follow-up of 10.5 years. Twenty prostheses failed. The estimated cumulative survival rate was 90.7%, 84.8%, 69.9%, and 66.2% at 5, 10, 15, and 20 years, respectively. The failure of tooth and/or implant abutments in key positions affected the survival of the prostheses. There were seven reasons for prostheses failure, with the loss of abutments exerting a significant influence. Bruxism was possibly associated with failures. Prostheses with cantilevers did not show a statistically significant higher failure rate. No group had a general higher prevalence of technical complications in comparison to the other groups. CONCLUSIONS: Although combined tooth-implant-supported FDPs are an alternative treatment option, this study has found that across 20 years of service nearly 35% the prostheses may fail.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".