Measuring the Unilateral Cleft Lip Nasal Deformity: Lateral Deviation of Subnasale Is a Clinical and Morphologic Index of Unrepaired Severity
Bibliographic record
Abstract
OBJECTIVE: Objective measurement of pre-operative severity is important to optimize evidence-based practices given that the wide spectrum of presentation likely influences outcomes. The purpose of this study was to determine the correlation of objective measures of form with a subjective standard of cleft severity. DESIGN: 3D images were ranked according to severity of nasal deformity by 7 cleft surgeons so that the mean rank could be used as the severity standard. PATIENTS: 45 patients with unilateral cleft lip and 5 normal control subjects. INTERVENTIONS: Each image was assessed using traditional anthropometric analysis, 3D landmark displacements, and shape-based analysis to produce 81 indices for each subject. MAIN OUTCOME: The correlation of objective measurements with the clinical severity standard. RESULTS: Lateral deviation of subnasale from midline was the best predictor of severity (0.86). Other strongly-correlated anthropometric measurements included columellar angle, nostril width ratio, and lateral lip height ratio (0.72, 0.80, 0.79). Almost all shape-based measurements had tight correlation with the severity standard, however, dorsum deviation and point difference nasolabial symmetry were the most predictive (0.84, 0.82). CONCLUSIONS: Quantitative measures of severity transcend cleft type and can be used to grade clinical severity. Lateral deviation of subnasale was the best measure of severity and may be used as a surrogate of uncoupled premaxillary growth; it should be recorded as an index of pre-operative severity with every cleft lip repair. The correlation of other measures evaluated clarify treatment priorities and could potentially be used to grade outcomes.
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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 | 0.003 | 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".