Color match of ultra‐translucency multilayer zirconia restorations with different designs and backgrounds
Bibliographic record
Abstract
Abstract Purpose The purpose of this in vitro study was to assess the color match of ultra‐translucency multilayer zirconia restorations with different designs and backgrounds. Materials and methods Thirty ultra‐translucency multilayer zirconia crown specimens were made in VITA classical shade B2 for a prepared maxillary central incisor. The specimens were divided into three groups based on the restoration design: veneered zirconia with a trestle design (VZT), veneered zirconia with a dentin core design (VZD), and full‐contour zirconia (FCZ). In the groups VZT and VZD, the zirconia specimens were layered with a feldspathic veneering ceramic. The specimens were seated on five different backgrounds: shade B2 composite resin, shade B2 zirconia, copper‐colored metal alloy, silver‐colored metal alloy, and the prepared central incisor. CIELab values of the labial middle sections of the crown specimens were measured with a spectrophotometer. Color differences between the specimens and a shade B2 VITA classical tab (as a control) were calculated from the ΔE*ab formula and compared with an acceptability threshold (ΔE*ab = 3.7) to be clinically explicated. Results Mean ΔE*ab values ranged between 1.17 and 8.48. The restoration design, the background type, and their interaction affected the ΔE*ab (p < 0.001). The mean ΔE*ab values for VZT with all backgrounds and for VZD with the silver‐colored metal background were greater than the threshold (p < 0.001), whereas the mean ΔE*ab values for VZD with the other backgrounds and FCZ with all backgrounds were less than the threshold (p = 1). Conclusions Restoration design and background type affected the color match of ultra‐translucency multilayer zirconia restorations. VZT restorations on all backgrounds and VZD restorations on a silver‐colored metal background showed color mismatches. However, VZD restorations on the other backgrounds and FCZ restorations on all backgrounds demonstrated color matches.
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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 | 0.001 |
| 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) | 0.001 | 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".