Robot-assisted nipple-sparing mastectomy: systematic review.
Bibliographic record
Abstract
BACKGROUND: The growing volume of studies of robot-assisted nipple-sparing mastectomy requires critical assessment. This review synthesizes the data on safety, feasibility, oncological and cosmetic outcomes, and patient-reported outcome measures (PROMs) for robot-assisted nipple-sparing mastectomy. METHODS: A systematic review was performed using MEDLINE, MEDLINE In-Process/ePubs, Embase/Embase Classic, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, LILACS, PubMed, ClinicalTrials.Gov, WHO ICTRP and the grey literature. Original studies reporting on patients with breast cancer or at increased risk of breast cancer undergoing robot-assisted nipple-sparing mastectomy were included. Risk of bias was assessed using the Institute of Health Economics Case Series Quality Appraisal Checklist. RESULTS: Of 7177 titles screened, eight articles were included, reporting on 249 robot-assisted nipple-sparing mastectomies in 187 women. The indication was either therapeutic (58·6 per cent) or prophylactic (41·4 per cent), with immediate reconstruction performed in 96·8 per cent. Surgical techniques followed a similar approach, with variations in incision, robot models, camera and insufflation. Postoperative morbidity included skin complications, lymphocele, infection, seroma, haematoma and skin ischaemia/necrosis. Complications specific to the nipple-areolar complex included ischaemia and necrosis. There were two conversions owing to haemorrhage, but no intraoperative deaths. Three patients had positive margins. Follow-up time ranged from 3·4 to 44·8 months. Locoregional recurrences were not observed. PROMs and objective cosmetic outcomes were reported inconsistently. Data on nipple sensitivity were not reported. CONCLUSION: Robot-assisted nipple-sparing mastectomy is feasible with acceptable short-term outcomes but it remains in the assessment phase.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".