Current Status of Suction Lipectomy in Canada: A Cross-Sectional Survey of Plastic Surgeons
Bibliographic record
Abstract
Background Suction lipectomy is the most common aesthetic surgery performed in North America. The safety of the procedure has recently been questioned, particularly with respect to large volume aspiration. Objective To delineate Canadian liposuction practice patterns and perceived complication rates. Subjects and Methods A survey was mailed to all plastic surgeons registered with the Canadian Society of Plastic Surgeons. The survey assessed surgeon demographics, hospital admission rates following liposuction, techniques used, wetting solutions, aspirate volumes and complications. Results A total of 322 surveys were mailed. The response rate was 44%. Of the 142 surgeons who responded, 119 regularly performed suction lipectomy. They reported that 70% of patients had suction lipectomy performed on an outpatient basis. Forty per cent of procedures were performed in private clinics. On average, surgeons performed 40 liposuctions/year without ancillary procedures and 27 liposuctions/year with ancillary procedures. Fifteen per cent of surgeons used ultrasound-assisted liposuction as an adjunct to traditional liposuction, although less than 1% used it exclusively. The ratio of aspirate to infiltrate was 0.25 to one in 45% of cases, 0.5 to one in 37%, one to one in 17% and one to one in fewer than 1%. In 69% of patients, 1 to 2 L was aspirated, while in fewer than 5% of patients, more than 3.5 L was aspirated. Early complication rates were as follows: hematoma and seroma 2.6%, infection 1.3% and skin loss 0.01%. Contour irregularity was the main late complication, occurring in 12% of patients. No cases of venous thrombosis, or pulmonary or fat emboli were reported. No deaths were reported. Conclusions Suction lipectomy is performed commonly by Canadian plastic surgeons, with the majority using a wet or superwet technique. In the vast majority of patients, fewer than 3500 cm 3 are aspirated. The complication rate is perceived to be low.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".