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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".