Facial attractiveness of skeletal class I and class II malocclusion as perceived by laypeople, patients and clinicians
Bibliographic record
Abstract
BACKGROUND: Physical attractiveness is dependent on facial appearance. The facial profile plays a crucial role in facial attractiveness and can be improved with orthodontic treatment. The aesthetic assessment of facial appearance may be influenced by the cultural background and education of the assessor and dependent upon the experience level of dental professionals. This study aimed to evaluate how the sagittal jaw relationship in Class I and Class II individuals affects facial attractiveness, and whether the assessor's professional education and background affect the perception of facial attractiveness. METHODS: Facial silhouettes simulating mandibular retrusion, maxillary protrusion, mandibular retrusion combined with maxillary protrusion, bimaxillary protrusion and severe bimaxillary protrusion in class I and class II patients were assessed by five groups of people with different backgrounds and education levels (i.e., 23 expert orthodontists, 21 orthodontists, 15 maxillofacial surgeons, 19 orthodontic patients and 28 laypeople). RESULTS: Straight facial profiles were judged to be more attractive than convex profiles due to severe mandibular retrusion and to mandibular retrusion combined with maxillary protrusion (all P<0.05). Convex profiles due to a slightly retruded position of the mandible were judged less attractive by clinicians than by patients and laypeople (all P<0.05). CONCLUSIONS: Convex facial profiles are less attractive than Class I profiles. The assessment of facial attractiveness is dependent on the assessor's education and background. Laypeople and patients are considerably less sensitive to abnormal sagittal jaw relationships than orthodontists.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".