Validated Composite Assessment Scales for the Global Face
Bibliographic record
Abstract
BACKGROUND: Twenty grading scales have been developed to assess age-related facial changes. Until now, the validity with regard to the patient's actual age and the clinical importance of combined measurement tools to describe facial aging was unclear. OBJECTIVE: To investigate the reliability and validity of a total face score and three global face assessment scales for estimated age, estimated aesthetic treatment effort, and signs of aging in the facial units. MATERIALS AND METHODS: Descriptive, reliability, correlation, and principal component analyses based on the assessment of 50 subjects by 12 raters using the 20 grading scales and the global face assessment scales. RESULTS: Inter- and intrarater reliability was high for the total face score and for the scales on estimated age and aesthetic treatment effort. Actual age was highly correlated with these three measures. Facial aging was indicated particularly by scales of the lower face. CONCLUSION: The aesthetic grading scales and global scales on estimated age and aesthetic treatment effort are reliable and valid instruments. The results suggest that a more-comprehensive evaluation of the human face and its age-related changes can help to identify important areas of facial aging and to define optimal aesthetic treatment strategies.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".