Validated Assessment Scales for the Lower Face
Bibliographic record
Abstract
BACKGROUND: Aging in the lower face leads to lines, wrinkles, depression of the corners of the mouth, and changes in lip volume and lip shape, with increased sagging of the skin of the jawline. Refined, easy-to-use, validated, objective standards assessing the severity of these changes are required in clinical research and practice. OBJECTIVE: To establish the reliability of eight lower face scales assessing nasolabial folds, marionette lines, upper and lower lip fullness, lip wrinkles (at rest and dynamic), the oral commissure and jawline, aesthetic areas, and the lower face unit. METHODS AND MATERIALS: Four 5-point rating scales were developed to objectively assess upper and lower lip wrinkles, oral commissures, and the jawline. Twelve experts rated identical lower face photographs of 50 subjects in two separate rating cycles using eight 5-point scales. Inter- and intrarater reliability of responses was assessed. RESULTS: Interrater reliability was substantial or almost perfect for all lower face scales, aesthetic areas, and the lower face unit. Intrarater reliability was high for all scales, areas and the lower face unit. CONCLUSION: Our rating scales are reliable tools for valid and reproducible assessment of the aging process in lower face areas.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".