Impact of gray background on tooth color shade matching: a comparison of visual and instrumental methods
Bibliographic record
Abstract
Objective: This study evaluates the impact of a gray background on visual tooth shade selection, focusing on various incisal translucency patterns in upper incisors. Material and Methods: Sixty-three clinicians assessed VITA 3D Master Shade Guide tabs representing right upper central incisors under different conditions, with or without a gray background. Translucency patterns (A, B, C) were considered, and standard tabs were defined using a clinical spectrophotometer. Statistical analyses, including repeated measures ANOVA and ordinal logistic regression, compared scores and agreement levels. Results: Darker tabs were selected for Case C, while Case B resulted in lighter tabs. A gray background increased lightness levels, enhancing agreement between visual and instrumental shade selection. Reduced agreements were noted in cervical areas and cases with higher incisal translucency. No significant difference was found among tooth thirds (p=.097). Conclusion: Using a gray background during tooth shade selection improved agreement between visual and instrumental shade selection. Incorporating this method can enhance tooth shade matching when relying on visual analysis. Introducing a cost-effective gray background can significantly improve agreement between visual and instrumental shade selection, addressing financial constraints associated with advanced tools. Clinicians can now implement a more reliable and accessible protocol, positively impacting the precision of esthetic restorations, especially in cases involving upper incisors. KEYWORDS Color; Color perception; Dental shade; Operative dentistry; Shade selection.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".