Are there any color match and color correlation between maxillary anterior teeth?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the color match and color correlation between maxillary anterior teeth. MATERIALS AND METHODS: color differences between similar regions of the same and different type teeth were calculated and compared with perceptibility and acceptability thresholds using 1-sample t test to evaluate color matches. Regression analyses assessed linear relationships between the color coordinates of similar regions of different type teeth. Percentages of different modes of the color match between the same specimen's teeth (2-tooth/3-tooth color match or color mismatch) were determined. RESULTS: values for different type teeth were mostly greater than 1.8 (p < 0.001), except for central and lateral teeth in middle (p = 0.29) and incisal (p = 0.75) regions and for lateral and canine teeth in cervical regions (p = 0.33). The 2-tooth color match showed the highest percentage (>50%). CONCLUSIONS: The same type teeth indicated color matches. Central and lateral teeth showed color matches in middle and incisal regions, while lateral and canine teeth disclosed color matches in cervical regions. The corresponding color coordinates of mismatched regions were linearly correlated. CLINICAL SIGNIFICANCE: In order to predict and determine the shade of maxillary anterior teeth and create natural colors for corresponding restorations, some tooth color relationships and equations are presented in this study.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".