Quality Assessment and Comparison of 3D-Printed and Milled Zirconia Anterior Crowns and Veneers: In Vitro Pilot Study
Bibliographic record
Abstract
Abstract The esthetic rehabilitation of a patient is a demanding yet rewarding procedure, improving the form, function, and well-being of a patient. Three-dimensional (3D) printed, or additive manufactured, zirconia has recently entered the dental space, but without a thorough assessment or comparison. This pilot study utilized digital impressions of two demonstration casts: Cast 1 prepared both central incisors for full ceramic crown coverage, while cast 2 had a lateral incisor (#22) prepared for a ceramic veneer. Both casts underwent digital scanning (Straumann CARES 3, Straumann, Basel, Switzerland) to create virtual STL models. Cast 1 had two full zirconia anterior crowns digitally designed, and Cast 2 had a zirconia veneer digitally designed, using Exocad GmbH software by a certified dental technician at Schulich Dentistry. The STL files were used for fabricating six milled zirconia crowns for central incisor (#21) and six 3D-printed zirconia crowns for the other central incisor (#11). Similarly, for Cast 2, milled and 3D-printed zirconia veneers were made for the prepared lateral incisor (#22). Statistical analysis employed Minitab 16.1.0 software to construct a 2 × 2 table for cross-tabulation and chi-squared analysis. This statistical approach assessed the relationship between restoration design and processing method. Cochran–Mantel–Haenszel test evaluated categorical variables considering different classification variables. Milled restorations showed minor variations, while 3D-printed units displayed consistency. Statistical tests found no significant associations. This in vitro study suggests 3D-printed zirconia for crowns and veneers meets precementation standards akin to conventionally milled restorations. Further research can assess its potential benefits for dentistry's efficiency, cost, and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".