Fiber‐reinforced composite partial fixed dental prostheses supported by short or extra‐short implants: A 10 year retrospective study
Bibliographic record
Abstract
INTRODUCTION: This study evaluated the 10-year survival and success of partial fixed dental prostheses (P-FDPs) fabricated with a milled fiber-reinforced composite (FRC) framework, supported by short or extra-short implants. METHODS: Patients restored with FRC P-FDPs supported by short or extra-short implants were included in this retrospective study. Kaplan-Meier analysis was used to calculate the survival and success rates of the prostheses. Univariate and multivariate Cox regression models, clustered to adjust for multiple implants and prostheses being placed in the same patient, were used to correlate changes in peri-implant bone levels with patient, implant, and prosthesis-related covariates. RESULTS: This study followed 121 FRC P-FDPs supported by 261 implants, placed in 96 patients. At 118 months in function, the P-FDP survival rate was 95.9% (95% CI: 87.5%-98.7%), and the success rate was 89.8% (95%CI: 80.4%-94.8%). Differences in prosthesis span length, abutment/pontic ratio, and the presence of distal extensions (cantilevers) did not affect the prosthetic outcomes. Bone levels around implants were stable, with an average rate of change of -0.01 ± 0.05 mm/month. Cox regression revealed that grafted sites were correlated with peri-implant bone loss, while longer prosthetic spans were correlated with bone gain. CONCLUSION: P-FDPs comprised of milled fiber-reinforced composite frameworks, supported by short and extra-short implants, had high survival and success rates for up to 10 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".