Fat Reduction in the Inner Thigh Using a Prototype Cryolipolysis Applicator
Bibliographic record
Abstract
BACKGROUND: Previous clinical studies have investigated cryolipolysis for noninvasive reduction of fat in abdomens, outer thighs, flanks, and backs. This study investigated feasibility of cryolipolysis for inner thigh treatment. OBJECTIVE: This pilot study evaluated a cryolipolysis flat cup vacuum applicator for treatment of inner thigh fat. METHODS: A prototype vacuum applicator was used to treat n = 11 subjects in a single-side inner thigh study. Cryolipolysis treatment was delivered to the larger thigh while the contralateral thigh served as a control. Follow-ups were conducted at 8 and 16 weeks. Equalization treatments were subsequently delivered to the contralateral thigh. Safety was assessed by monitoring side effects and adverse events. Efficacy was evaluated by ultrasound imaging, clinical photography, and patient surveys. RESULTS: Side effects were typical and resolved spontaneously. Efficacy was demonstrated with ultrasound measurements showing 83% of subjects attained some level of fat layer reduction. Normalized mean reduction in fat layer thickness was 20%, corresponding to 3.3 mm. Patient surveys revealed 91% were satisfied and 82% felt inner thigh cryolipolysis was comfortable. Clinical photographs revealed visible reduction in inner thigh contour after treatment. CONCLUSION: This study demonstrates feasibility of safe and efficacious cryolipolysis treatment to the inner thigh.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".