A Multicenter Evaluation of Paradoxical Adipose Hyperplasia Following Cryolipolysis for Fat Reduction and Body Contouring: A Review of 8658 Cycles in 2114 Patients
Bibliographic record
Abstract
BACKGROUND: Paradoxical adipose hyperplasia (PAH) is a rare, moderate-to-severe adverse event associated with cryolipolysis (CoolSculpting, CS). OBJECTIVES: The aim of this study was to describe the incidence, diagnosis, and treatment of PAH occurring after CS for nonsurgical fat reduction. METHODS: A multicenter evaluation of all patients who underwent CS treatment between January 2015 and December 2019 at 8 Canadian medical centers was conducted. Data abstracted included symptoms, management strategy, outcome, operator characteristics, device characteristics, patient characteristics, body region, and CS treatment details. Incidence of PAH was calculated based on the number of treatment cycles. RESULTS: Our findings revealed incidence rates between 0.05% and 0.39%, which are slightly higher than the manufacturer's quoted rate of 0.025% (1 per 4000 cycles). Incidence rates at all sites were dramatically reduced by over 75% with the implementation of newer models of CS units. Of patients who developed PAH, 55% were male and 77.8% were of European ethnic origin. The majority of cases (76.9%) were associated with older models of CS units. CONCLUSIONS: Development of PAH may be related to a combination of factors, including older models of CS units and applicators, as well as individual characteristics that predispose certain patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".