Cryolipolysis for Reduction of Arm Fat: Safety and Efficacy of a Prototype CoolCup Applicator With Flat Contour
Bibliographic record
Abstract
BACKGROUND: Cryolipolysis of the arms has been shown to be an effective but somewhat time-consuming process. OBJECTIVE: The study evaluated safety and efficacy of a contoured cup cryolipolysis applicator for reduction of arm fat. The prototype was designed to maximize tissue contact with the cooling surface to improve comfort, while reducing treatment time by 25 minutes. MATERIALS AND METHODS: Both arms were treated using a prototype device that delivered treatment in 35 minutes at -11°C. Photographic and ultrasound documentation was captured at baseline and 12 weeks post-treatment. Efficacy was assessed by photo review and measurement of fat reduction in ultrasound images. Immediately after 1, 4, and 12 weeks post-treatment, clinical assessments were performed to evaluate treatment areas and sensory alterations. RESULTS: Thirty women were enrolled and completed treatments to both arms. Ultrasound measurements found mean fat layer reduction of 3.2 mm with an SD of 2.7 mm. Blinded independent photo review found 85.2% correct identification of baseline photographs by at least 2/3 of reviewers. There were no unanticipated adverse device effects. Four study subjects experienced numbness in the treatment area beyond the 12-week visit that subsequently resolved without intervention. CONCLUSION: These data suggest that the CoolCup prototype applicator provides rapid, safe, and effective arm 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 | 0.001 | 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 | 0.000 |
| 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".