Nipple reduction - An adjunct to augmentation mammaplasty
Bibliographic record
Abstract
N ipple hypertrophy and its reduction have been described in a limited number of articles throughout the medical literature (1-8).Nipple hypertrophy is an uncommon esthetic problem.The epidemiology of this deformity remains largely unknown; it is mostly encountered in the Asian population and, occasionally, in Caucasians.It is sometimes familial, appearing after the onset of adolescence or following pregnancy, and persisting through menopause (1).It may lead to psychological distress because of its association with a negative esthetic image.As well, women may feel embarrassed by their hypertrophic nipples, which are hard to conceal under light clothing because of their prominence.No formal definition of nipple hypertrophy exists.Some clinicians have suggested that the normal female nipple is roughly 1 cm in diameter, with an almost equal amount of anterior projection (1).In cases of nipple hypertrophy, the size of the nipple may reach 2 cm or even more, and the shape usually becomes spherical.The aim of the correction is primarily to shorten the nipple length, while secondarily establishing harmony between its diameter size and projection.The patient seeking a correction for nipple hypertrophy is usually also concerned about the functional results of the surgery, in addition to the esthetic outcome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".