Preferences of Different Breast Reduction Techniques
Bibliographic record
Abstract
Background: Breast reduction is a common and safe procedure with predicted cosmetic outcomes. Many techniques have evolved over the recent decades. Aims: The aim of this study is to determine what type of breast reduction techniques are currently preferred among board certificated Saudi plastic surgeons and assess the surgeons' satisfaction, surgeon-reported patient satisfaction, and complication rates post breast reduction with the preferred techniques. Materials and Methods: This is a cross-sectional questionnaire-based study. The questionnaire was adapted from previously published studies and distributed to a small group before full-scale distribution to Saudi plastic surgeons by email and communication groups. Results: The mean age of the participants was 45.4 (± 8.9). Most participants were males (82%), and the majority held a Saudi board (44%), and 20% held a Canadian board. Significant differences between different board certifications, held fellowship, and years of experience emerged in terms of surgical preferences. The two most common complications reported by surgeons were suture splitting (34%) and excess scarring (24%). Conclusions: In Saudi Arabia, inverted T resection patterns with superior or superomedial pedicle designs are the standard techniques used in breast reduction, with higher satisfaction rates and fewer complications. Surgical preferences were significantly different between surgeons based on their training and held fellowships.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".