3D-printed versus conventionally milled zirconia for dental clinical applications: Trueness, precision, accuracy, biological and esthetic aspects.
Bibliographic record
Abstract
This systematic review aimed to compare the clinical outcome, internal gap, trueness, precision, and biocompatibility of 3D-printed (AM) compared to milled (SM) zirconia restorations. A thorough search of Internet databases was conducted up to September 2023. The search retrieved studies compared AM zirconia to SM zirconia restorations regarding clinical outcome, fit, trueness, precision, and biocompatibility. Of 1736 records, only 59 were screened for eligibility, and 22 records were included in this review. The quality of studies was assessed using the revised Cochrane risk-of-bias tool (ROB2), Newcastle Ottawa scale (NOS), and the Modified Consort Statement. One clinical study showed a moderate risk of bias and one has a low risk of bias. All laboratory studies revealed some bias concerns. Short-term observation showed 100% survival with no signs of periodontal complications. 3D-printed zirconia crowns showed statistically significant lower ΔE and a better match to adjacent teeth (p≤0.5). The fit, trueness, and precision vary with the printing technique and the tooth surface. 3D-printed zirconia crowns provide better aesthetic color and contour match to adjacent natural teeth than milled crowns. Both 3D printing and milling result in crowns within the clinically acceptable internal and marginal fit. Except for nanoparticle jetting, the marginal gap of SM crowns was smaller than AM crowns, however, both were clinically acceptable. Laminate veneers might be more accurately produced by 3D printing. 3D-printed axial surface trueness was better than milled axial surfaces. Long-term RCTs are recommended to confirm the clinical applicability of 3D-printed restorations. Internal fit and gap, precision, and trueness are fundamental requirements for successful dental restorations. Both techniques produce restorations with clinically acceptable marginal and internal fit. Axial surfaces and narrow or constricted areas favored 3D-printed than conventionally milled zirconia.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".