3‐Year Randomized Controlled Prospective Clinical Trial on Different CAD‐CAM Implant Abutments
Bibliographic record
Abstract
BACKGROUND: Zirconia abutments were introduced to restore esthetic regions and showed sufficient stability to support implant restorations. Nonetheless, to date the observation periods are shorter than those of titanium abutments. PURPOSE: To assess the survival of implant crowns supported by computer aided design-computer aided manufacturing (CAD-CAM) abutments after 3 years. MATERIALS AND METHODS: Fifty-six patients were selected for this prospective clinical study. Each patient received at least 1 titanium implant for a total of 89 fixtures. A two-stage surgical technique and no additional soft or hard tissue graft were used. The implants were randomly divided into 3 groups receiving different CAD-CAM abutments: titanium, titanium nitride, and zirconia. Zirconia or metal-ceramic crowns were used as final restorations. Cementation was the baseline and the restorations were checked after 6 months, 1, 2, and 3 years, assessing any mechanical complication. Statistical analyses were performed to evaluate the 3-year success rates. RESULTS: Five failures were reported in the zirconia group; all the failed restorations showed fractures of the abutment connection. Four failures occurred in posterior regions and one more occurred while screwing the abutment. Titanium and titanium nitride abutments had significantly higher 3-year success rates than zirconia abutments (p < .05). CONCLUSIONS: Atlantis titanium and titanium nitride abutments showed optimal clinical performances after 3 years. Conversely, Atlantis zirconia abutments should be avoided to restore posterior regions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".