Current Findings Regarding Zirconia Implants
Bibliographic record
Abstract
PURPOSE: The present article aims to analyze the available clinical data on the survival and success rate of dental zirconia implants (ZI). MATERIAL AND METHOD: Studies (2006-2011) listed in the bibliography were obtained by using the key words "zirconia, zirconium, implants, dental, clinical" and combinations of these in different databases and on the internet. These articles served as a basis for the article. RESULTS: A total of 17 clinical studies were found, involving 1,675 implants and 1,274 patients. In 16 studies, one-piece implant systems were investigated. The survival rates for ZI range from 74-98% after 12-56 months, with success rates between 79.6-91.6% 6-12 months after prosthetic restoration. However, the design of most of the studies show considerable shortcomings, and only low evidence level. CONCLUSION: The small number of studies and the limited period of observation permit only a qualified statement on the clinical success of ZI. The results available to date indicate that ZI are inferior to titanium implants (TI) with regard to survival and success rates. Well-conducted long-term studies are urgently needed to permit a meaningful assessment of the survival or success rates of ZI and a statement concerning their application as an alternative to TI.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".