A Simplified Approach to Breast Reduction Using the Medial Pedicle
Bibliographic record
Abstract
Background: Breast reduction is a common procedure for plastic surgery. The authors have adopted a modified technique using the medial pedicle, with markings using a 15-9-9 framework and a methodical step-wise approach. Objectives: This study introduces the 15-9-9 framework as a design for medial pedicle breast reductions that is easy to perform and teach, with favorable outcomes. Methods: Markings using the 15-9-9 framework were used, describing the mosque dome and medial pedicle length and width. The technique was performed in day surgery under general anesthesia. Patients were followed up for 1 year, with photographs taken at each visit and complications recorded. A retrospective review of 80 patients between November 2013 and July 2019 was completed in a single-surgeon's practice. Results: (23-32). The average planned postoperative sternal notch to areola distance was 22 cm (19-26 cm) and sternal notch to nipple distance was 24 cm (21-28 cm). The average duration of the surgical procedure was 3.4 hours. An average of 464 g (90-1210 g) was removed from each breast. Complication rates were low with minor fat necrosis (14%), T-junction breakdown (10%), hematoma (3.8%), dog ear formation (3.8%), junctional necrosis (2.5%), and partial nipple loss (1.3%). One patient had a cerebrovascular accident in the late postoperative period. Aesthetically pleasing results were achieved postoperatively. Conclusions: This technique using the 15-9-9 framework is simple to learn, perform, and teach with overall aesthetically pleasing outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".