Objective Assessment of the Unilateral Cleft Lip Nasal Deformity Using Three-Dimensional Stereophotogrammetry: Severity and Outcome
Bibliographic record
Abstract
BACKGROUND: Optimization of care to correct the unilateral cleft lip nasal deformity is hampered by lack of objective measures to quantify preoperative severity and outcome. The purpose of this study was to develop a consensus standard of nasal appearance using three-dimensional stereophotogrammetry; determine whether anthropometric measurements could be used to quantify severity and outcome; and determine whether preoperative severity predicts postoperative outcome. METHODS: The authors collected facial three-dimensional images of 100 subjects in three groups: 45 infants before cleft lip repair; the same 45 infants after cleft lip repair; and 45 children aged 8 to 10 years with previous repairs. Five additional age-matched unaffected control subjects were included in each group. Seven expert surgeons ranked images in each group according to nasal appearance. The rank sum score was used as consensus standard. Anthropometric analysis was performed on each image and compared to the rank sum score. Preoperative rank and anthropometric measurements were compared to postoperative rank. RESULTS: Interrater and intrarater reliability was excellent (intraclass correlation coefficient, >0.76; Pearson correlation, >0.75) on each of the three image sets. Columellar angle, nostril width ratio, and lateral lip height ratio were highly correlated with preoperative severity and moderately correlated with postoperative nasal appearance. Postoperative outcome was associated with preoperative severity (rank and anthropometric measurement). CONCLUSIONS: Consensus ranking of preoperative severity and postoperative outcome can be achieved on three-dimensional images. Preoperative severity predicts postoperative outcomes. Columellar angle, nostril width ratio, and lateral lip height ratio are objective measures that correlate with consensus ratings by surgeons at multiple ages.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".