Pilot study of dermal and subcutaneous fat structures by MRI in individuals who differ in gender, BMI, and cellulite grading
Bibliographic record
Abstract
BACKGROUND/AIMS: Puckered, dimply skin on the thighs, hips, and buttocks is known as cellulite. The cause of cellulite is not known, although there are a number of different hypotheses. In this study, we use magnetic resonance (MR) micro-imaging to study cellulite skin. To the best of our knowledge, this is the first reported MR study of cellulite. METHODS: High-resolution in vivo MR images of the postlateral thigh skin of two male groups and four female groups were obtained. Subjects were grouped according to their body mass index (BMI) and cellulite grade. A qualitative assessment of how MRI can be used to differentiate skin tissue at different levels of cellulite grading was performed. RESULTS: We found that changes in skin architecture with cellulite can be visualized by in vivo MR micro-imaging. The skin fat layers beneath the dermis and down to the level of muscles are well visualized in the images. Also, the diffuse pattern of extrusion of underlying adipose tissue into dermis is clearly imaged, and was found to correlate with cellulite grading. We also show that other skin tissue parameters such as (a) the percentile of adipose vs. connective tissue in a given volume of hypodermis and (b) the percentile of hypodermic invaginations inside the dermis are correlated with cellulite grade. CONCLUSION: MR images can be interpreted to measure tissue parameters correlated with cellulite. Considering that we had only three subjects in each group, the achievements of this pilot study were highly satisfactory. We have shown that the in vivo micro-MR is a technique able to detect the effects of cellulite and gender. This study can be extended for further investigations of drugs and/or medical devices for cellulite treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".