Keyhole Pattern for Preoperative Marking for Reduction Mammaplasty
Bibliographic record
Abstract
BACKGROUND There are many techniques used for reduction mammaplasty; however, the most frequently performed procedures result in an inverted T scar. Preoperative marking is an important step for the success of the procedure, especially for surgeons at the initial learning stage. However, there is no consensus regarding the best method. In 1981, Strömbeck designed a pattern for preoperative marking for reduction mammaplasty. This pattern provides stable parameters that promotes an acceptable symmetry marking. OBJECTIVE To evaluate the use of the Strömbeck pattern for preoperative marking for reduction mammaplasty. METHODS Fifty-seven patients who underwent reduction mammaplasty between April 2006 and April 2007 were prospectively evaluated. Patient ages ranged from 17 to 61 years; the mean body mass index was 22.2 kg/m 2 . After defining the standard landmarks of the breast, preoperative markings were made using the Strömbeck pattern. Breast reduction surgery was performed under local anesthesia with sedation. Postoperative results were evaluated according to a numerical visual analogue scale, at the seven-, 15-and 30-day follow-up periods. The Student's t test and the Kruskal-Wallis test were used for statistical analysis (P<0.05). RESULTS The mean weight of resected breast tissue was 317.5 g for the right breast and 305.8 g for the left breast (P=0.17). Scores obtained using a visual analogue scale showed a progressive increase in the scores during the postoperative follow-up period (P<0.0001). CONCLUSION The use of the Strömbeck pattern enabled surgeons to perform reduction mammaplasty with good postoperative results as seen in the follow-up periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".