A Rating Scale for Obtaining Specific, Actionable Evaluations of Nasolabial Aesthetics after Unilateral Cleft Lip Repair
Bibliographic record
Abstract
BACKGROUND: Surgeons pursuing improvement in the aesthetic outcomes of their cleft lip repairs may benefit from a granular scale evaluating individual objectives of the repair. METHODS: A working group of 9 surgeons convened to develop an assessment scale for nasolabial aesthetics after unilateral cleft lip repair. The group identified objectives of the repair that could be evaluated using two-dimensional facial photographs. Scale items were developed to appraise success or failure in achieving each objective. Scale items were iteratively tested and refined. The scale was subsequently implemented as part of a Continuing Medical Education course that included self-evaluation and peer-to-peer education, culminating in the formation of individual plans for improvement. RESULTS: Twelve distinct objectives of unilateral cleft lip repair were identified, of which 10 could be evaluated using photographs routinely obtained in clinical practice. A comprehensive scale was developed, incorporating these 10 objectives. Each scale item takes the form of a binary (yes/no) question evaluating a specific aesthetic concept, with accompanying reference images. Intrarater reliability for each item ranged from moderate to substantial (kappa value, 0.57 to 0.81). Interrater reliability ranged from fair to substantial (kappa value, 0.27 to 0.81). When implemented in a Continuing Medical Education course, the scale enabled surgeons to identify specific opportunities for improvement in their repair and specific surgical maneuvers to adopt in pursuit of these improvements. CONCLUSIONS: A new scale for evaluating outcomes of unilateral cleft lip repair is presented. The scale provides specific, actionable evaluations for individual objectives of the repair.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".