Development and Validation of a Photonumeric Scale for Evaluation of Infraorbital Hollows
Bibliographic record
Abstract
BACKGROUND: A validated scale is needed for objective and reproducible comparisons of infraorbital hollows (i.e., tear troughs) before and after treatment in practice and clinical studies. OBJECTIVE: To describe the development and validation of the 5-point photonumeric Allergan Infraorbital Hollows Scale. METHODS: The scale was developed to include an assessment guide, verbal descriptors, morphed images, and real subject images for each grade. The clinical significance of a 1-point score difference was evaluated in a review of image pairs representing varying differences in severity. Interrater and intrarater reliability was evaluated in a live-subject validation study (N = 297) 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% confidence interval] absolute score difference, 0.90 [0.79-1.02] for clinically different image pairs and 0.33 [0.19-0.46] for not clinically different pairs). Intrarater agreement between the 2 validation sessions was substantial (mean weighted kappa = 0.79). Interrater agreement was substantial during the second rating session (0.70, primary end point). CONCLUSION: The Allergan Infraorbital Hollows Scale is a validated and reliable scale for physician rating severity of hollowing in the infraorbital area.
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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 | 0.029 | 0.037 |
| 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".