HIPEC + EPIC versus HIPEC‐alone: Differences in major complications following cytoreduction surgery for peritoneal malignancy
Bibliographic record
Abstract
INTRODUCTION: Peritoneal metastases (PM) can be treated with cytoreduction surgery (CRS) with intraoperative heated intraperitoneal chemotherapy (HIPEC) plus or minus early postoperative intraperitoneal chemotherapy (EPIC). HIPEC + EPIC may be associated with more complications than HIPEC alone. METHODS: A prospective database of consecutive patients undergoing CRS + HIPEC ± EPIC at the University of Calgary between February 2000 and May 2011 was reviewed. Patient, tumor, and perioperative variables included peritoneal cancer index (PCI), completeness of cytoreduction (CCR) score, HIPEC ± EPIC type, and grade III/IV complications. RESULTS: 198 patients had a CCR score of 0/1 and received: (1) HIPEC mitomycin C + EPIC 5-fluorouracil for 5 days (n = 85; February 2000-January 2008); or (2) HIPEC oxaliplatin with IV 5-fluorouracil + no EPIC (n = 113; February 2008-May 2011). Clinicodemographics were similar except PCI was higher in the HIPEC-alone group (mean PCI 22 vs. 17; P = 0.02). The rate of grade III/IV complications was higher in the HIPEC + EPIC group (44.7% vs. 31.0%; P = 0.05). On multivariate logistic regression only HIPEC + EPIC and PCI > 26 were associated with an increased rate of complications. CONCLUSION: In patients with PM, the use of EPIC, in combination with CRS and HIPEC, is associated with an increased rate of complications. Surgeons should consider using HIPEC only (without EPIC).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".