Fluorescence guided total robotic parietal peritonectomy, cytoreductive surgery and closed HIPEC
Bibliographic record
Abstract
Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) is increasingly used for peritoneal surface malignancies, shifting from open to minimally invasive approaches for lower Peritoneal Carcinomatosis Index(PCI) cases. Robotic surgery's benefits include enhanced visualization, ergonomics, and reduced complications, supporting its adoption in oncologic procedures. We'll outline a step-by-step technique for Fluorescence-Guided Total Robotic Parietal Peritonectomy, Cytoreductive Surgery, and closed HIPEC in advanced peritoneal carcinomatosis. In this demonstration, we outline the procedure for performing a robotic total parietal peritonectomy with cytoreductive surgery and HIPEC in a 45-year-old patient diagnosed with stage IIIC ovarian cancer and peritoneal carcinomatosis, following three cycles of neoadjuvant chemotherapy (NACT). We highlight the utilization of Indocyanine Green-Near Infrared (ICG-NIR) guided real-time imaging to assess peritoneal deposits post-chemotherapy and guide lymph node dissection. In this case, with a PCI of 15, complete cytoreduction (CC0) was achieved using a minimally invasive robotic approach with HIPEC. The procedure had a short docking time of 22 minutes and a total console time of 300 minutes. HIPEC lasted 90 minutes, and the total operative time, including surgery and HIPEC, was 410 minutes with minimal blood loss. The patient was discharged on Post operative day 3, showcasing the benefits of this approach in achieving rapid recovery and short hospital stays for peritoneal surface malignancies. Robotic technology like ICG-NIR imaging and advanced tools has boosted the speed and safety of CRS and HIPEC. Success hinges on careful patient selection. The future promises even less invasiveness and better outcomes for peritoneal surface malignancies with minimally invasive and multimodal approaches.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".