Creation and validation of a photonumeric scale for assessment of lip fullness
Bibliographic record
Abstract
BACKGROUND: Validated, objective clinical scales are needed to assess aesthetic improvement of the lips after augmentation with dermal fillers. OBJECTIVE: To develop a lip fullness rating scale and establish its reliability for grading subjects in clinical trials or routine practice, and sensitivity for detecting clinically meaningful changes. METHODS: The Teoxane Lip Fullness Scale (TLFS), a proprietary, 5-grade photonumeric scale, was developed by clinical experts based on real subject photographs and was validated through both photographic and live subjects' evaluation. RESULTS: Clinician intra- and inter-rater agreement for the TLFS was substantial to almost perfect. Mean intra-rater weighted Kappa score between the two rounds of photographic validation was 0.92, and inter-rater agreement was substantial with an ICC of 0.93 for the combined rounds. Average intra-rater weighted Kappa score and inter-rater ICC for the live validation were equally high, reaching 0.91 and 0.89 respectively. Additionally, evaluators identified clinically significant differences between photographs of subjects presenting a 1-grade or 2-grade difference on the scale in 90% and 98% of cases, respectively. CONCLUSIONS: The intra-rater Kappa scores and inter-rater ICC met their pre-determined acceptance criteria of >0.70 in the photographic and live validation. The TLFS was shown to be a repeatable and reproducible Clinician Reported Outcome (Clin-RO) for healthcare providers to classify lip fullness both in clinical trials and in routine patient care. A 1-grade difference on the TLFS can detect a clinically meaningful difference in lip fullness.
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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 | 0.000 |
| 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 | 0.000 |
| 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".