Tooth Shade Preferences among the General Public
Bibliographic record
Abstract
PURPOSE: To identify laypersons' most preferred tooth shade as a function of observer and patient factors, namely patients' skin shade and observers' socio-demographics. MATERIALS AND METHODS: Two online surveys using computer-designed perioral images with different shades of the skin and teeth were distributed to participants in Montreal (Canada) and San Francisco (USA). The first survey (n = 120) was designed to assess public preferences of tooth shade value, hue and chroma as a function of the skin color of the perioral image (model), and the demographic characteristics of the observer. The first survey included 6 sets of 9 identical perioral images. A different skin shade (from very dark to very light) was used for each set of images, and each set of images presented teeth with different tooth shades which included three different levels of value (2M1, 3M1, 4M1), hue (3L1.5, 3M1, 3R1.5), and chroma (3M1, 3M2, 3M3) of the Vita 3D Master shade guide. Participants were asked to choose their preferred image for each category (value, hue, chroma) within each set of skin shades. A second survey was performed to pinpoint the tooth shade that is preferred the most by the general public. In this survey, images with four tooth shades (1M1, 2M1, 3M1, 4M1) and 6 skin shades were distributed (n = 70). Ordinal logistic regression was used to identify significant predictors of preferred tooth shades. RESULTS: Most of the participants preferred teeth with the highest value (54%), a neutral hue (59%) and the lowest chroma (89%). About 75% of the participants preferred 1M1 the lightest tooth shade over other shades regardless of their demographics or skin color of the model. Among the observer-related variable, age was the most significant predictor of people preferred tooth shade (p = 0.019). CONCLUSION: This study showed that there are common preferences in terms of tooth shade value, hue and chroma among participants regardless of demographic variables and facial skin shades. This data could guide dentists for tooth shade selection in the clinical practice.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".