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 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 | 0.055 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".