Esthetic evaluation of natural teeth in anterior maxilla using the pink and white esthetic scores
Bibliographic record
Abstract
BACKGROUND: Natural teeth in the anterior maxilla are critical in determination the esthetic outcome of single implant prosthesis. PURPOSE: The present study aimed to explore aesthetics of natural teeth in the anterior maxilla using the Pink Esthetic Score/White Esthetic Score (PES/WES) index. Additionally, inherent weak spots of natural teeth and high-risk parameters of prostheses were also considered. MATERIAL AND METHODS: This cross-sectional study was performed by photographic analysis. RESULTS: A total of 102 subjects and 306 teeth (the right incisor, lateral incisor and canine) were included. The grand means of the PES and WES were 12.92 and 8.75, respectively. The score of soft tissue margin, soft tissue contour and outline/volume of the crown were significantly lower than other variables. The PES and WES showed a downward trend with age. Most of the PES/WES values of the females exceeded those of the males. CONCLUSION: The average level of natural teeth in PES and WES assessment were around 13 and 9, respectively. The soft tissue margin, soft tissue contour and outline/volume of the crown were high-risk parameters for the esthetic outcomes of implant reconstructions. Underlying factors, such as age and gender, contributed to the esthetics of natural teeth change.
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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 | 0.001 | 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) | 0.003 | 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".