Bibliographic record
Abstract
BACKGROUND: An important part of preoperative assessment in breast reduction surgery is to locate the site of the nipple-areola complex for the newly structured breast. Inappropriate location is difficult to correct secondarily. Traditional methods of nipple localization taught and practiced suggest the nipple to be located anterior to the inframammary fold. Trying to project this point on the anterior surface of the breast requires either large calipers or feeling the posteriorly placed finger on the anterior surface of a large breast. This certainly introduces some subjectivity to the calculation. OBJECTIVES: To introduce an easy and accurate method of nipple localization to reduce the learning curve for trainee surgeons. METHODS: Aesthetic placement of the nipples is at the lower angles of an equilateral or a short isosceles triangle on the chest with its apex at the sternal angle. This triangle can be thought of as two right-angled triangles with their Y-axis on the median plane. The base and vertical limb are measured, and the hypotenuse is calculated. The location of the lower angle is marked on the anterior surface of the breast and represents the new position of the nipple. RESULTS: Forty patients had nipple localization performed in the above-described manner, with satisfactory placement of the nipple-areola complex. CONCLUSIONS: The above technique introduces some objective measurements to the localization of the nipple in breast reduction surgery. It is easy to practice, and infuses confidence in trainees marking their initial breast reductions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".