How to Preserve the Nipple Erogenous Sensation in Breast Lifting and Reduction Mammaplasties
Bibliographic record
Abstract
Preservation of nipple sensitivity after breast reduction is an important goal to achieve. However, most breast reduction techniques focus more on breast aesthetics and nipple areolar complex vascularity, ignoring nipple sensitivity particularly nipple erogenous sensation. In fact, the nipple nervous system includes a tactile sensation system and an erogenous sensation system, the latter being less described and commonly overlooked by plastic surgeons performing breast reductions. The erogenous sensation is supplied by the IVth, Vth and VIth intercostal nerves who run laterally on the surface along the muscular aponeurosis. The total posterior pedicle breast reduction technique described by Richard Moufarrege in 1982 consists of dissecting the skin away from the breast tissue offering free access to all breast quadrants. This technique is known for its robust blood supply to the nipple areolar complex, for its preservation of the breastfeeding function and for its low rate of long term complications such as pseudoptosis. In this article, we also demonstrate that the Moufarrege Total Posterior Pedicle preserves the nipple erogenous sensation among women undergoing breast reduction using this technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".