Safety of high-dose mitomycin C vs oxaliplatin HIPEC for peritoneal metastases
Bibliographic record
Abstract
GOALS: oxaliplatin. METHODS: This retrospective cohort study analyzed all patients with appendiceal and colorectal PM treated at a tertiary-care hospital from 2014 to 2024. RESULTS: Among 282 patients, 48 (17.0 %) received high-dose MMC and 234 (83.0 %) received oxaliplatin. Patient demographics and oncological characteristics were similar (p > 0.05). High-dose MMC had significantly more toxic events (35.4 % vs 14.1 %, p < 0.001), greater CTCAE median toxicity grade (2 vs 1, p < 0.001), higher hepatic cytolysis (2.1 % vs 0.0 %, p = 0.027), increased neutropenia (27.1 % vs 4.3 %, p < 0.001), more gastric perforations (4.2 % vs 0 %, p = 0.002) as well as one case of HIPEC toxicity-related death due to neutropenic enterocolitis (2.1 % vs 0.0 %, p = 0.380). Oxaliplatin resulted in more hematomas (12.0 % vs 2.1 %, p = 0.040), higher need for parenteral nutrition (94.0 % vs 83.3 %, p = 0.012), and longer duration of nutritional support (12.3d vs 8.6d, p = 0.020). High-dose MMC had higher abdominal sepsis rates (6.3 % vs 1.3 %, p = 0.030). Severe complications, reintervention, ICU transfers, and 90-day mortality were similar (p > 0.05). Length of stay was shorter for high-dose MMC (14.7d vs 17.7d, p = 0.031). CONCLUSION: High-dose MMC was associated with increased HIPEC toxicity, primarily neutropenia-related. Clinicians must balance the benefits and drawbacks of high-dose MMC and oxaliplatin to provide an optimal and individualized treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".