Complications of Aesthetic Liposuction Performed in Isolation: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Aesthetic liposuction represents one of the most commonly performed cosmetic procedures worldwide. The purpose of this article is to examine and synthesize reported complication rates and explore the analytical prospect of possible patient or procedure-related predictive factors associated with specific complications. Methods: A systematic review was performed using the Pubmed, Cochrane, and Embase databases in line with specific criteria set to ensure an accurate assessment of complication rates; extracted data was synthesized through a random-effects model and meta-analysis of proportions. Results: A total of 60 studies were included in the meta-analysis, representing 21,776 patients undergoing aesthetic liposuction. Most studies followed an observational design. The overall complication rate was 12% (95% confidence interval [CI] 8%, 16%). When stratifying according to specific complications, the incidence of contour irregularities was determined to be 2% (95% CI 1%, 2%), seroma 2% (95% CI 1%; 2%), hematoma 1% (95% CI 0%, 1%), surgical site infection 1% (95% CI 1%, 2%), fibrosis or induration 1% (95% CI 1%, 2%), and pigmentary changes 1% (95% CI 1%, 1%), among others. A meta-regression to identify patient- or procedure-related factors associated with greater complication rates proved infeasible given the nature of the available data. Conclusion: Overall, liposuction demonstrated a relatively low complication rate profile, however, a considerable degree of heterogeneity exists within the examined literature preventing the recognition of predictive risk factors. While this calls for efforts to establish consensus on unified methods of outcomes reporting, the present meta-analysis can serve to provide practitioners with an evidence-based reference to improve informed consent and inform clinical guidelines, specifically pertaining to the incidence of commonly encountered complications in aesthetic liposuction, of which presently available survey studies and database queries remain devoid.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".