Anthropometrics versus Experts’ Subjective Analysis of Cleft Severity and PSIO Outcomes in Unilateral Clefts: A New Grading System
Bibliographic record
Abstract
BACKGROUND: The severity of unilateral cleft lip significantly influences surgical outcomes, yet no standardized system exists to classify cleft severity or assess the impact of presurgical infant orthopedics (PSIO). This study proposes an objective classification system, integrating anthropometric measurements with expert evaluations. METHODS: Deidentified pre-PSIO and post-PSIO photographs of 50 infants with unilateral cleft lip from the Smile Train Express database were analyzed. Three anthropometric parameters-nostril width ratio (NWR), columellar angle (CA), and subnasale lateral displacement (SN)-were measured. An expert panel of orthodontists and surgeons independently rated cleft severity and PSIO outcomes, in a structured 3-stage process. Severity thresholds were established through consensus, and interrater agreement was analyzed using weighted kappa. RESULTS: Consensus-derived thresholds categorized NWR, CA, and SN into 4 severity levels. Interrater agreement for cleft severity improved across stages, reaching nearly perfect levels in stage 3 (pre-PSIO weighted kappa, 0.91; post-PSIO weighted kappa, 0.93). Although pre-PSIO agreement was similar between surgeons and orthodontists, post-PSIO assessments showed greater variability. PSIO had a disproportionate effect on nasal morphology (CA) compared with maxillary segments (NWR and SN), with severe NWR and SN frequently coexisting with mild CA. The proposed classification system demonstrated substantial reliability, aligning at least 2 parameters within the same severity subclassification. CONCLUSIONS: This study introduces a standardized classification system for cleft severity and PSIO outcomes, demonstrating strong interrater reliability. By integrating anthropometric data with expert assessments, it provides a reproducible framework for clinical and research applications. Further refinements, including intraoral measurements and 3-dimensional imaging, may enhance its precision and applicability.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".