CEREC Chairside System to Register and Design the Occlusion in Restorative Dentistry: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: The aim of this review was to update the literature with regard to the digital methods available by CEREC Chairside system to register and design the occlusion, to report their efficacy and technical innovations in the field of Restorative Dentistry. A search strategy was performed using the key-words: "virtual articulator," or "CAD-CAM and occlusal recording," or "CAD-CAM and occlusion register," or "CAD-CAM and occlusal contacts," or "CAD-CAM and prosthesis." MATERIAL AND METHODS: Inclusion criteria comprised studies evaluating the use of digital methods available by CEREC System for occlusal registration and design during prosthodontics treatment. PubMed and Cochrane library and reference lists were searched up to January 2016. RESULTS: The search resulted in 280 articles after removing duplicates. Subsequently, 233 records were excluded and 49 studies were selected for reading in full. Eleven articles were considered eligible for the systematic review (4 in vitro and 7 clinical studies). CONCLUSION: Scientific evidence suggests that digital methods were accurate to register and design the occlusion of dental prostheses. Nevertheless, further clinical studies are required to establish a conclusion with regard to its accuracy in prosthodontics treatment. CLINICAL SIGNIFICANCE: Digital technologies allow the design of occlusal surfaces of CAD-CAM fabricated prostheses using innovative approaches. This systematic review aimed to update the literature to help dentists determine the most appropriate digital method to register and design the occlusal surface of CAD-CAM crowns. (J Esthet Restor Dent 28:208-220, 2016).
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".