Bibliographic record
Abstract
BACKGROUND: This article reviews the three breast dimensions and how they can be changed. The first two dimensions constitute the breast footprint. The third dimension is the shape of the breast on the footprint. METHODS: All four breast footprint borders are reviewed along with the third dimension, which is the breast shape and how it sits on that footprint. An analysis of the "normal" position of the footprint and the "normal" shape of the breast is given. It is important for the surgeon to understand how change in each of the parameters can be effected. The upper and lateral breast borders are relatively mobile, but the inferior and medial breast borders are relatively fixed. All four borders can be changed with certain surgical maneuvers, and these have been measured and analyzed. The breast is a skin structure that is held in place by skin/fascial zones of adherence, and the breast itself is mobile over the pectoralis fascia. RESULTS: Measurements before and after breast augmentation, breast reduction, mastopexy, and mastopexy-augmentation have been obtained so that the surgeon can better predict results. The change in suprasternal notch-to-nipple distance and the change in suprasternal notch-to-inframammary fold distance have been measured. CONCLUSION: Being able to explain the issues and the potential changes makes it easier for a surgeon to manage patients' expectations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".