Clinical Evaluation of 209 All‐Ceramic Single Crowns Cemented on Natural and Implant‐Supported Abutments with Different Luting Agents: A 6‐Year Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The Procera AllCeram™ system (Nobel Biocare AB, Göteborg, Sweden) is a valid alternative to metal-ceramic restorations. However, limited long-term data of its use for single crowns on natural and implant-supported abutments are available. PURPOSE: The present study aimed at evaluating the clinical performances of Procera AllCeram single crowns in both anterior and posterior regions of the oral cavity either on natural tooth or implant abutments over a period of 6 years. MATERIALS AND METHODS: Two hundred nine single crowns were fabricated and used in 112 patients. Zinc phosphate and resin luting agents were used to cement the restorations. The crowns were evaluated according to the California Dental Association's quality assessment system. RESULTS: Three crowns were lost at follow-up. Of the 206 restorations, which completed the 6-year follow-up, 9 crowns were affected by mechanical complications and 7 crowns failed. All surviving crowns were ranked as either excellent or acceptable. Cumulative survival and success rates of 95.2 and 90.9%, respectively, were recorded. CONCLUSIONS: Within the limitations of the present study, Procera AllCeram crowns proved to be a reliable clinical option to restore both anterior and posterior missing teeth either on natural or implant abutments. The resin cement used in the present study performed better than the zinc phosphate luting agent.
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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 | 0.002 |
| 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 | 0.000 |
| 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".