Development and Validation of a Photonumeric Scale for Evaluation of Facial Fine Lines
Bibliographic record
Abstract
BACKGROUND: A validated scale is needed for objective and reproducible comparisons of facial fine lines before and after treatment in practice and clinical studies. OBJECTIVE: To describe the development and validation of the 5-point photonumeric Allergan Fine Lines Scale. METHODS: The Allergan Fine Lines Scale was developed to include an assessment guide, verbal descriptors, morphed images, and real subject images for each scale grade. The clinical significance of a 1-point score difference was evaluated in a review of multiple image pairs representing varying differences in severity. Interrater and intrarater reliability was evaluated in a live subject validation study (N = 289) completed during 2 sessions occurring 3 weeks apart. RESULTS: A score difference of ≥1 point was shown to reflect a clinically significant difference (mean [95% CI] absolute score difference, 1.06 [0.92-1.21] for clinically different image pairs and 0.50 [0.38-0.61] for not clinically different pairs). Intrarater agreement between the 2 live subject validation sessions was almost perfect (weighted kappa = 0.85). Interrater agreement was substantial during the second rating session (0.76, primary end point). CONCLUSION: The Allergan Fine Lines Scale is a validated and reliable scale for physician rating of severity of superficial fine lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.042 |
| 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 | 0.001 |
| 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".