Exploring mandibular asymmetry: insights from visual perception using eye-tracking technology
Bibliographic record
Abstract
The visual attention provides an objective perspective on how a stimulus take attention. In dentistry, one of the important facial determinants in esthetic perception is the mandibular asymmetry. The study aimed to evaluate the eye movements of the orthodontists and non-professionals on the images with different severity of mandibular asymmetry using eye tracking technology. The eye movements of 26 orthodontists and 30 non-professionals were captured. Thirty images were visually evaluated for the presence of mandibular asymmetry by two orthodontists. 2 mm, 4 mm, 6 mm, and 8 mm chin deviation were simulated on the images and the images without asymmetry were considered as control group. A total of 50 photographs from 10 individuals were included in the study. Participants’ eye movements were recorded using an Eyelink 1000 plus eye-tracking device (Sr-Research, Canada). Repeated Measures Analysis of Variance (ANOVA) was used for statistical comparisons. The number of fixations on the lower lip-chin area in either the right or left direction did not show a statistically significant difference. (F(1,000;59,000) = 2.133, p > 0.05, ). Time to first fixation was faster to the lower lip-chin area in 8 mm asymmetry condition compared to 2 mm (F(1,2) = 31.423, p < 0.05, η p 2 = 0.940). Orthodontists made less fixations before the lower lip-chin area in 8 mm condition compared to 2 mm (F(1,2) = 20.758, p < 0.05, η p 2 = 0.912). While the direction of mandibular asymmetry did not affect voluntary attention, an increase in asymmetry, regardless of profession, attracted more attention to the lower lip-chin area. While the 8 mm asymmetry caught the involuntary attention of orthodontists, the same did not occur in non-professionals.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".