Retrospective Clinical Evaluation of 86 Procera AllCeram™ Anterior Single Crowns on Natural and Implant‐Supported Abutments
Bibliographic record
Abstract
BACKGROUND: The Procera AllCeram (Nobel Biocare AB, Göteborg, Sweden, and Procera Sandvik AB, Stockholm, Sweden) technique is one alternative to metal-ceramic restorations. However, few long-term evaluations of its use for single crowns on natural and implant-supported abutments are available. PURPOSE: The aim of the present study was to assess the clinical performance of Procera AllCeram single crowns when placed in aesthetic sites supported by either natural teeth or implants over a period of 48 months. MATERIALS AND METHODS: Eighty-six single crowns were fabricated and used in 51 patients. The restorations were examined according to the California Dental Association's quality assessment system. RESULTS: One crown was lost after 20 months of follow-up. Of the 85 restorations that completed the 48-month follow-up, only one crown (1.2%) showed a veneering porcelain chip. All crowns were ranked as either excellent or acceptable. The success rates of single crowns supported by natural tooth and implant-supported abutments were 100% and 98.3%, respectively; the total crown success rate was 98.8%. CONCLUSION: Within the limitations of the present study, Procera AllCeram crowns proved to be a reliable therapeutic choice for the restoration of anterior teeth on both natural and implant-supported abutments. The hybrid glass-ionomer cement used in the present study appeared to be a reliable 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".