Minimal access nipple-sparing mastectomy – the current European landscape
Bibliographic record
Abstract
Minimal access nipple-sparing mastectomy (M-NSM), performed with endoscopic systems or with surgical robot assistance, is a novel alternative to the classic approach to nipple-sparing mastectomies. Leading advancements in M-NSM have primarily come from Asia. We gather experts' opinions from six European countries to establish the current status of M-NSM in Europe. An eight-question survey was designed to explore M-NSM's historical background and current standing in various local settings. We collected data from 6 European countries, including Italy, Spain, France, Switzerland, Belgium, and Poland. The number of centers offering M-NSM procedures in each reported country ranges 1-9. The number of procedures performed annually in four centers exceeds 10. In all reported countries, current national breast cancer recommendations do not include M-NSM, and this procedure is not explicitly covered by any of the national health care providers. All experts have indicated the need for training in M-NSM surgery as a primary way to incorporate these techniques as a standard procedure. Minimal access nipple-sparing mastectomy is still a tool used by a narrow group of specialists in Europe. The main obstacle to broader implementation remains the extra cost of M-NSM, which requires reimbursement from the health care providers. Training courses, data collection, and demonstration of its benefits are the key to promoting M-NSM among breast surgeons and patients.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".