Laypersons' perceptions of the esthetics of visible anterior occlusion.
Bibliographic record
Abstract
OBJECTIVE: To analyze 6 different types of visible anterior occlusion, exclusive of extraoral facial features, in terms of their esthetic appeal, as perceived by 91 randomly selected adult laypersons with different levels of education in Lima, Peru. METHODS: Photographic images of 3 examples of each occlusion type (open bite [OB], deep bite [DB], crossbite [CB], end-to-end bite [EE], crowded bite [CwB] and ideal bite [IB]) were prepared. Evaluators used a visual analogue scale (VAS) to rate their perceptions of the esthetic appeal of each view. Analysis of variance (ANOVA) was used to test differences in mean scores among occlusion types and to test the effect of evaluator characteristics on perceived attractiveness. RESULTS: The mean score was highest for IB, followed by EE, DB, CB, CwB and OB. Oneway ANOVA with Bonferroni post hoc test showed no difference between the highestscoring occlusion types (IB and EE, p > 0.99); each of the remaining groups was significantly different from both IB and EE (p < 0.001). The scores for DB, CB, CwB and OB were progressively lower, although not significantly different from one another (p > 0.05). Univariate ANOVA to determine the effects of evaluator characteristics (age, level of education, gender and interaction between level of education and gender) showed that gender was a significant factor (p < 0.034) for all bite groups except OB. Level of education was significant only for OB (p = 0.020) and age only for EE (p = 0.011). The interaction between level of education and gender was significant for all bite types (p < 0.046). CONCLUSIONS: Lay evaluators identified ideal and EE bite occlusal relationships as esthetically most pleasing. They judged DB, CB, CwB and OB bite relationships as less esthetically pleasing but did not differentiate between these types of malocclusions.
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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 | 0.000 | 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) | 0.003 | 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".