Objective Measurements for Grading the Primary Unilateral Cleft Lip Nasal Deformity
Bibliographic record
Abstract
BACKGROUND: The purpose of this prospective study was to develop and validate an objective means of grading the presurgical unilateral cleft lip nasal deformity. Our hypotheses are that expert cleft surgeons can reliably rank patients according to their subjective assessment of the degree of unilateral cleft lip nasal deformity and that objective anthropometric measurements correlate consistently with this subjective assessment. METHODS: Forty consecutive infants with unilateral cleft lip nasal deformity were subjectively ranked according to the degree of deformity on standardized presurgical photographs by four senior cleft surgeons. Internal agreement was assessed. Correlations between presurgical anthropometric parameters and the subjective panel rankings were determined. To test the reproducibility of subjective ranking, a random subset of images (n = 15) was also ranked by four external expert cleft surgeons of international reputation. RESULTS: There was extremely high agreement of subjective ranking by the expert panels (interclass correlation coefficient, 0.95 for the internal panel and 0.94 for the external panel) and between the two panels (interclass correlation coefficient, 0.97). The nostril width ratio was the most predictive of both internal and external rank scores (r = 0.76, p < 0.0001; and r = 0.81, p = 0.0003, respectively). The columellar angle was also highly predictive of both internal and external expert rank scores (r = 0.73, p < 0.0001; and r = 0.79, p = 0.0005, respectively). CONCLUSIONS: Experts are reliably able to subjectively rank patients according to the degree of nasal deformity. Measures of the columellar angle and the nostril width ratio vary in a linear fashion with the perceived deformity and may serve as independent and objective indicators of presurgical severity of unilateral cleft lip nasal deformity.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".