Current Surgical Management of Colorectal Cancer Liver Metastases: A Mini Review
Bibliographic record
Abstract
C olorectal cancer is responsible for 10% of cancer related deaths and is the third most common cancer type in men and the second in women. Metastatic disease will develop in nearly 60% of patients with colorectal cancer, half of which will be located in the liver. At least 30% of the patients who underwent potentially curative primary tumor resection have liver micrometastases. A PubMed search of relevant articles published up to 2011 was performed to identify current information about colorectal liver metastases regarding diagnosis and management with emphasis to surgery. The results shows that hepatectomy is the only potentially curative option resulting in 5-year survival for one third of the patients, but only 10 - 30% of the patients are suitable for a radical procedure. Indications include ability to resect the tumor without residual disease, with removal of at least 1 cm of healthy tissue and clear margins, and the absence of extrahepatic metastatic disease. Specialized centers achieve mortality rate less than 5% and morbidity 20 - 50%. 5-year survival following hepatic resection is 25 - 40% with mean survival of 28 - 40 months. We conclude that current management of colorectal cancer liver metastases requires a multidisciplinary approach. Operative strategies and novel techniques that achieve wider excision areas with minimal blood loss make hepatectomy safe and effective. doi:10.4021/jcs106e
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".