Esthetics and smile characteristics evaluated by laypersons
Bibliographic record
Abstract
OBJECTIVE: To collect data regarding Canadian laypersons' perceptions of smile esthetics and compare these data to US data in order to evaluate cultural differences. MATERIALS AND METHODS: Using Adobe Photoshop 7, a digital image of a posed smile of a sexually ambiguous lower face was prepared so that hard and soft tissue could be manipulated to alter buccal corridor (BC), gingival display (GD), occlusal cant (OC), maxillary midline to face discrepancy (MMFD), and lateral central gingival discrepancy (LCGD). Adult Canadian laypersons (n = 103) completed an interactive computer-based survey of 29 randomized images to compare smile preferences for these variables. The custom survey was developed to display fluid, continuously appearing modifiable smile variables using MATLAB R2008 for presentation. These data were compared with previously published data for US laypersons. Statistical inference was determined using Wilcoxon rank sum tests. RESULTS: Canadian laypersons were more sensitive in detecting deviations from ideal and had a narrower range of acceptability thresholds for BC, GD, OC, MMFD, and LCGD. Ideal esthetic values were significantly different only for BC. CONCLUSIONS: It appears that cultural differences do exist related to smile characteristics. Clinically significant differences in the preference of the smile characteristics were found between Canadian and US laypersons. Canadian laypersons, on average, were more discriminating to deviations from ideal and had a narrower range of acceptability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".