Evolution and Adaptations of Robotic DIEP Flap Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The integration of robotic technology into surgical procedures has gained considerable attention for its promise to enhance a variety of clinical outcomes. Robotic deep inferior epigastric perforator (DIEP) flap harvest has emerged as a novel approach for autologous breast reconstruction. This systematic review aims to provide a comprehensive overview of the current techniques, outcomes, and complications of robotic DIEP flap surgery. METHODS: A systematic literature search was conducted after PRISMA 2020 guidelines across databases including PubMed, Embase, Google Scholar, and Web of Science from 2000 to 2023. Articles exploring robotic DIEP flap harvest for breast reconstruction were assessed to compare operative techniques, clinical outcomes, and complications. The risk of bias was evaluated using ROBINS-I and the Newcastle-Ottawa scale. RESULTS: Fourteen studies involving 108 patients were included. Three studies used a totally extraperitoneal (TEP) technique, whereas 11 studies used a transabdominal preperitoneal (TAPP) approach. Preoperative planning utilized computed tomography angiography and magnetic resonance angiography imaging. The mean robotic operative time was 64 minutes, with total operative times averaging 574 minutes for TAPP and 497 minutes for TEP. The mean length of stay was 5 days, and the mean fascial incision length was 3 cm. Overall complication rate was 14.9%, with no significant difference compared with conventional DIEP flap procedures. CONCLUSION: Robotic DIEP flap harvest is a promising technique that may reduce postoperative pain and limiting abdominal donor site morbidity. Potential limitations include longer operative times, variable hospital stays, and increased costs.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".