Skin characteristics: normative data for elasticity, erythema, melanin, and thickness at 16 different anatomical locations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The clinical use of non-invasive instrumentation to evaluate skin characteristics for diagnostic purposes and to evaluate treatment outcomes has become more prevalent. The purpose of this study was to generate normative data for skin elasticity, erythema (vascularity), melanin (pigmentation), and thickness across a broad age range at a wide variety of anatomical locations using the Cutometer(®) (6 mm probe), Mexameter(®) , and high-frequency ultrasound in a healthy adult sample. METHODS: We measured skin characteristics of 241 healthy participants who were stratified according to age and gender. Sixteen different anatomical locations were measured using the Cutometer(®) for maximum skin deformation, gross elasticity, and biological elasticity, the Mexameter(®) for erythema and melanin, and high-frequency ultrasound for skin thickness. Standardized measurement procedures were applied for all participants. RESULTS: The means and standard deviations for each measured skin characteristic for females and males across five different age groups (20-29, 30-39, 40-49, 50-59, 60-69, and 70-85 years old) are presented. As previously described, there were variations in skin characteristics across age groups, anatomical locations, and between females and males highlighting the need to use site specific, age and gender matched data when comparing skin characteristics. CONCLUSION: The reported data provides normative data stratified by anatomical location, age, and gender that can be used by clinicians and researchers to objectively determine whether patients' skin characteristics vary significantly from healthy subjects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it