doi:10.5402/2011/763245 Research Article Ten-Year Survival after Liver Resection for Colorectal Metastases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Copyright © 2011 Saleh Abbas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Liver resection in metastatic colorectal cancer is proved to result in five-year survival of 25–40%. Several factors have been investigated to look for prognostic factors stratifications such as resection margins, node involvement in the primary disease, and interval between the primary disease and liver metastases. Methods. We searched MEDLINE and EMBASE for studies that reported ten-year survival. Metaanalysis was performed to analyse the effect of recognised prognostic factors on cure rate for colorectal metastases. The meta-analysis was performed according to Ottawa-Newcastle method of analysis for nonrandomised trials and according to the guidelines of the PRISMA. Results. Eleven studies were included in the analysis, which showed a tenyear survival rate of 12–36%. Factors that have favourable impact are clear resection margin, low level of CEA, single metastatic deposit, and node negative disease. The only factor that excluded patients from cure is the positive status of the resection margin. Conclusion. Predicted ten-year survival after liver resection for colorectal metastases varies from 12 to 36%. Only positive resection margins resulted in no 10-year survivors. No patient can be excluded from consideration for liver resection so long the result is negative margins. 1.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".