Cryolipolysis of the Arms and Inner Thighs Shows Similar Treatment Outcomes in Chinese Individuals Compared to White Individuals Treated in a Prior Study: The XinCOOL Study
Bibliographic record
Abstract
Background: Studies of predominantly White participants show that cryolipolysis reduces subcutaneous fat in the arms and inner thighs, but none have specifically tested for similar outcomes in participants of Chinese descent. Objectives: This study assessed the safety and effectiveness of cryolipolysis treatment for noninvasive subcutaneous fat reduction of arms and inner thighs in participants of Chinese descent to assess equivalence to results seen in a prior study of White participants. Methods: Replicating a similar study design, participants of first- or second-generation Chinese descent underwent cryolipolysis treatment of arms and/or inner thighs. Effectiveness was assessed using pretreatment and posttreatment photographic review by blinded, independent experts, investigator-assessed caliper measurements, and participant satisfaction 12 weeks posttreatment. Safety was assessed throughout. Results: ). Overall, 76.4% and 70.0% of pretreatment photographs of arms and pairs of inner thighs, respectively, were correctly identified by at least 2 of 3 reviewers. The mean reduction from baseline in caliper-measured fat thickness was 6.5 mm for arms and 6.6 mm for inner thighs, and the majority of participants (>60%) were satisfied with the treatment. No adverse events were reported. Conclusions: Cryolipolysis is a well-tolerated, effective means of noninvasive fat reduction of arms and inner thighs in participants of Chinese descent. The results from this study show similar effectiveness and safety in Chinese participants compared with White participants treated in a prior study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".