Peritoneal carcinomatosis index predicts survival in colorectal patients undergoing HIPEC using oxaliplatin: a retrospective single-arm cohort study
Bibliographic record
Abstract
BACKGROUND: Peritoneal carcinomatosis (PC) from colorectal cancer is associated with poor prognosis. Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) has improved survival for patients with colorectal peritoneal carcinomatosis. However, standardization of HIPEC protocols, including which chemotherapeutic agent to use, is lacking in the literature. Therefore, we sought to report survival outcomes from colorectal cancer patients undergoing CRS/oxaliplatin-based HIPEC at our institution over the last 10 years. METHODS: Colorectal PC patients treated with CRS/oxaliplatin-based HIPEC 2004-2015 were included. Demographic, clinical, and oncologic data were abstracted from the medical record. Overall (OS) and disease-free survival (DFS) were calculated using Kaplan-Meier analysis. Univariate/multivariate Cox regression analysis was done. RESULTS: Laparotomy was performed in 113 patients for colorectal PC; 91 completed a curative intent CRS/HIPEC. At 3 and 5 years, OS for the CRS/HIPEC cohort was 75% and 55%, and DFS was 50% and 25%, respectively. On multivariate analysis, incremental increases in peritoneal carcinomatosis index (PCI) were associated with worse OS (p = 0.0001) and DFS (p = 0.0001). Grade III/IV complications were also associated with worse OS. CONCLUSIONS: A standardized regimen of CRS and oxaliplatin-based HIPEC for colorectal PC is effective with favorable OS and DFS and acceptable complication rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".