Three-Dimensional Morphologic Nasal Surface Characteristics that Predict the Extremes of Esthetics in Patients with Repaired Cleft Lip and Palate
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were (1) to develop imaging methods and objective numeric parameters to describe nose morphology, and (2) to correlate those parameters with nasal esthetics for patients with clefts. METHODS: A total of 28 patients with repaired complete unilateral cleft lip and palate (CUCLP) and 20 age- and gender-matched individuals without clefts were identified. A panel of orthodontists rated and ranked nasal esthetics from nose casts for the cleft group. Best and worst esthetic cleft groups were established from the cast assessments. Three-dimensional surface coordinates of the casts were digitally mapped with an electromagnetic tracking device. Digitized nasal images were oriented, voxelated, sliced, and mathematically curve-fitted. Maximum difference, percent area difference, and maximum and minimum derivative differences between cleft and noncleft and between right and left nose sides were calculated. Differences in parameters between groups were assessed with the use of analysis of variance (ANOVA) and t tests, and correlations with esthetics were assessed with the Spearman rank correlation test. RESULTS: Differences were seen between cleft and noncleft and best and worst esthetic groups for all four parameters (p < .05). The best esthetic cleft group had (1) lower percent area difference (p < .0001), (2) lower maximum difference (p < .001), and (3) smaller differences in slope of the nose in the coronal plane (p < .0001) than the worst esthetic cleft group. CONCLUSIONS: Maximum difference and maximum derivative difference and, to a lesser degree, percent area difference can be used to identify differences between cleft and noncleft nasal morphology and to assess levels of nasal esthetics for patients with CUCLP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".