Liver Resection for Non-Colorectal, Non-Carcinoid, Non-Sarcoma Metastases: A Multicenter Study
Bibliographic record
Abstract
BACKGROUND: The role of liver resection for non-colorectal, non-neuroendocrine, non-sarcoma (NCNNNS) metastases is ill-defined. This study aimed to examine the oncologic outcomes of liver resection in such patients. METHODS: A retrospective analysis of liver resection for NCNNNS metastases was performed at two large centers. Liver resection was offered selectively in patients with stable disease. Oncologic outcomes were examined using the Kaplan-Meier method. RESULTS: Fifty-two patients underwent liver resection for NCNNNS metastases. Overall 5-year survival was 58%. Five-year survival was 85% for breast metastases, 66% for ocular melanoma, 83% for other melanomas, 50% for gastro-esophageal metastases, and 0% for renal cell carcinoma metastases. A contemporary colorectal liver metastasis cohort had a survival of 63% (p=0.89). CONCLUSIONS: Liver resection is an effective option in the management of selected patients with NCNNNS metastases which have been deemed stable. Five-year survival rates were comparable to that of a contemporary cohort of patients with colorectal liver metastases in carefully selected patients. Further, larger studies are required to help identify potential prognostic variables and aid in decision-making in this heterogeneous population.
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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 | 0.002 | 0.002 |
| 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".