Practice Profiles in Breast Reduction: A Survey Among Canadian Plastic Surgeons
Bibliographic record
Abstract
BACKGROUND: Breast reduction is an increasingly common procedure performed by Canadian plastic surgeons. Recent studies in the United States show that use of the inferior/central pedicle inverted T scar method is predominant. However, it is unknown what the practice preferences are among Canadian plastic surgeons. OBJECTIVE: The goal of the present study was to assess trends in breast reduction surgery among Canadian surgeons, including patient selection criteria, surgical techniques and outcomes. METHOD: Surveys were distributed to plastic surgeons at the Canadian Society for Plastic Surgery meetings in 2005 and 2006. Completed surveys were obtained from 140 respondents, and results were analyzed with Excel and SAS software. RESULTS: There was a 40% response rate. The majority of surgeons (66%) used more than one technique for breast reduction. Most commonly, surgeons use the inverted T scar technique (66%) followed by vertical scar techniques (26%). The most popular vertical scar techniques included the Hall-Findlay (14%) and Lejour (13%) methods. Most surgeons (55%) reported complication rates of less than 5% and the most common complication reported was wound dehiscence. There was no difference in overall complication rates between inverted T scar and vertical scar surgeries. The majority of surgeons (98%) carried out breast reduction either exclusively as day surgery or in combination with same-day admission. Breast reduction performed as day surgery resulted in cost savings of $873 per patient. CONCLUSIONS: Canadian plastic surgeons are performing more vertical scar breast reductions than American surgeons. However, both groups rely predominantly on inverted T scar techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".