Cryolipolysis and Skin Tightening
Bibliographic record
Abstract
BACKGROUND: Anecdotally, there have been reports of skin tightening after cryolipolysis, but this has not been studied or reported in the literature. OBJECTIVE: This clinical evaluation of patients treated with cryolipolysis in the thighs, abdomen, arms, and back assesses changes to skin texture, laxity, and cellulite at 2 study centers. METHODS: From the Vancouver site, a comprehensive review of cryolipolysis treatments was performed to assess treatment areas and retreatments. While reviewing data, investigators were struck by the noticeable skin tightening shown in clinical photographs. Subsequently, a survey of Vancouver patients was conducted to assess changes to skin texture and laxity. At the Marina del Rey site, subjects undergoing a clinical study for lateral thigh cryolipolysis were evaluated for changes to skin texture, laxity, and cellulite. RESULTS: Independent assessments by patients and investigators found consistent improvement in skin texture and laxity for treatments to the outer thighs, abdomen, arms, and back. Outer thighs also showed mild-to-moderate improvement in cellulite. CONCLUSION: This clinical evaluation demonstrates consistent improvement in skin texture, laxity, and cellulite after cryolipolysis as independently assessed by patients and investigators. Prospective clinical studies should be conducted to objectively study and quantify skin tightening after cryolipolysis.
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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 | 0.003 |
| 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) | 0.003 | 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".