Impact of Body Composition on Postoperative Outcomes in Patients Undergoing Robotic Nipple-Sparing Mastectomy with Immediate Breast Reconstruction
Bibliographic record
Abstract
Nipple-areolar complex (NAC)-related complications are common during nipple-sparing mastectomy (NSM), with obesity as a risk factor. Although the incidence of NAC-related complications after robotic NSM (RNSM) with immediate breast reconstruction (IBR) is lower than that after conventional NSM, it remains one of the most unwanted complications. We aimed to evaluate body composition-based risk factors for NAC-related complications after RNSM with IBR. Data of 92 patients with breast cancer who underwent RNSM with IBR using direct-to-implant or tissue expander from November 2017 to September 2020 were analyzed retrospectively. Risk factors for NAC-related complications were identified with a focus on body composition using preoperative transverse computed tomography at the third lumbar vertebra level. Postoperative complications were assessed for 6 months. The most common complication was NAC ischemia, occurring in 15 patients (16%). Multivariate analysis revealed a low skeletal muscle index/total adipose tissue index (SMI/TATI) ratio as an independent NAC ischemia risk factor. An increase in the SMI/TATI ratio by one decreased the incidence of NAC ischemia by 0.940-fold (p = 0.030). A low SMI/TATI ratio is a risk factor for postoperative NAC ischemia in patients undergoing RNSM with IBR for breast cancer. Preoperative body composition-focused evaluation is more valuable than simple body mass index assessment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".