The Esthetic Effect of Veneered Zirconia Abutments for Single‐Tooth Implant Reconstructions: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to test whether or not veneering of the submucosal part of zirconia abutments can positively influence the esthetic outcome compared with nonveneered zirconia abutments; to evaluate the influence of the mucosal thickness on the esthetic outcomes of the veneered and nonveneered abutments; and to evaluate the thickness of the peri-implant mucosa compared with the thickness of the gingiva of contralateral tooth sites. MATERIALS AND METHODS: Forty-four single-tooth implants in 44 patients were randomly restored with either cemented (CR) or screw-retained (SR) reconstructions based on white zirconia abutments (CR-W, SR-W) or pink-veneered zirconia abutments (CR-P, SR-P). Esthetic outcome measurements were performed based on a spectrophotometric evaluation of the peri-implant mucosal color. In addition, the thickness of the mucosa was measured. A two-way analysis of variance was conducted to test the effect of veneering (pink vs white) and mucosa thickness (<2 mm vs ≥2 mm) on the calculated color difference ΔE for pooled data of CR and SR reconstructions (p < .05). RESULTS: Analyses grouping the sites according to veneering of the abutments and mucosal thickness demonstrated less discoloration for sites with a veneered abutment irrespective of the mucosal thickness: ΔE 4.50 ± 1.93 (<2 mm) and ΔE 6.88 ± 2.45 (≥2 mm); CR-P, SR-P) compared with sites without veneering ΔE 9.72 ± 3.82 (<2 mm; CR-W, SR-W) and ΔE 8.31 ± 2.98 (≥2 mm). The differences between veneered and nonveneered abutments were significant (p = .032). CONCLUSIONS: Veneering of zirconia abutments with pink veneering ceramic positively influenced the peri-implant mucosal color.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| 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".