Cytoreduction and heated intraperitoneal chemotherapy for colorectal cancer: Are we excluding patients who may benefit?
Bibliographic record
Abstract
BACKGROUND: Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) are increasingly used to treat peritoneal carcinomatosis from colorectal cancer. It is still relatively unknown which poor prognostic factors to avoid in order to optimize patient selection for CRS + HIPEC. METHODS: Between February 2003 and October 2011, 68 consecutive colorectal cancer patients who underwent CRS + HIPEC with a complete cytoreduction were identified from a prospective database. Survival analysis was performed using the Kaplan-Meier method, with log rank testing of differences between groups. Multivariate analysis was conducted using Cox proportional hazard regression. RESULTS: Median follow-up was 30.3 (range, 2-88) months amongst survivors. Patients with a peritoneal cancer index (PCI) of 10 or less showed improved survival over those with a PCI of 11 or higher (P = 0.03). No difference in survival was seen for the other potentially poor prognostic variables including lymph node status, synchronous peritoneal disease, peri-operative systemic chemotherapy, and rectal cancer primary. CONCLUSIONS: A low PCI was associated with improved survival. Complete CRS + HIPEC appears to result in similar survival outcomes regardless of delivery of peri-operative systemic chemotherapy. Rectal origin, lymph node status, and synchronous peritoneal disease should not be used as an absolute exclusion criteria for CRS + HIPEC based on current data.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".