Response to “Periareolar Augmentation/Mastopexy: How Does it Measure Up?”
Bibliographic record
Abstract
Dr Swanson’s letter1 commenting on our Featured Operative Technique paper2 on periareolar augmentation-mastopexy raises interesting issues regarding this challenging yet useful surgical technique. Dr Swanson is correct in stating that we have been “champions” of vertical scar breast surgery; however, no operation is perfect. Even the vertical scar mastopexy/reduction, a workhorse for breast surgery, has its limitations and shortcomings. Patients with breast ptosis present with differing anatomic variations. Understanding these variations and the variety of surgical options available allows us to deal with patients who have different anatomy and differing needs.3-5 Many authors have written on the usefulness of periareolar mastopexy.4, 6-16 When we see the excellent results demonstrated in these publications, it is appropriate for other surgeons who may be experiencing difficulty attaining a good result with this operation to question the validity of a procedure that they are unable to reproduce. This may cause the struggling surgeon to conclude that the problem must be the technique itself. However, if some surgeons are able to attain a satisfactory result with a certain procedure that others are not able to reproduce, the problem is usually not the procedure itself but rather the application of the technique that is required to achieve a good result.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.055 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".