Cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy with oxaliplatin for peritoneal carcinomatosis arising from colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Peritoneal carcinomatosis (PC) from colorectal cancer is associated with a poor prognosis. Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) have improved survival compared to systemic chemotherapy. We evaluate the results of this treatment in our institution. METHODS: Treatment consisted of complete CRS followed by HIPEC with oxaliplatin (460 mg/m(2) ) in 2 L/m(2) of D5W at 42°C during 30 min. RESULTS: From 2004 to 2011, 40 patients with PC from colorectal cancer were included and 25 CRS + HIPEC were performed. Six patients had a negative second-look surgery and nine had unresectable disease. Median follow-up was 22.8 months. Overall 3- and 5-year survival rates for the cohort were 56% and 33%. The 3- and 5-year overall survival rates were 61% and 36% for HIPEC group, 82% and 67% for patients with negative second-look, and 22% and 0% for the unresectable group (P = 0.0087). 3-year disease-free survival for HIPEC group was 22%. Major complication and mortality rate for HIPEC group were 20% and 4%. Peritoneal carcinomatosis index (P = 0.0374) and lymph node status (P = 0.027) were prognostic indicators. CONCLUSIONS: CRS + HIPEC with oxaliplatin for PC from colorectal cancer is an effective treatment with encouraging survival results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".