A Comparison of Five-Year Survival Rates Between Thermal Ablation and Hepatic Resection for Colorectal Cancer Metastasis to the Liver: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A significant proportion of colorectal liver metastases (CRLMs) are unresectable. This study compares liver resection with thermal ablation. Methods: The included studies enrolled patients with either resectable or unresectable tumors, depending on tumor characteristics, institutional protocols, and clinical eligibility. All included studies specifically assessed liver metastases from colorectal adenocarcinoma. Results: The findings of this review found no significant difference in 5-year overall and disease-free survival rates between both approaches (relative risk (RR) = 0.84 (0.54, 1.30), P = 0.35, I2 = 94%) and (RR = 1.00 (0.32, 3.13), P = 0.99, I2 = 89%), respectively. However, tumor recurrence was higher in the ablation group (odds ratio (OR) = 1.66 (1.06, 2.62), P = 0.03, I2 = 10%). Both groups had similar complication rates (OR = 0.34 (0.09, 1.21), P = 0.08, I2 = 84%). GRADE certainty was very low for all outcomes. The study quality was heterogeneous, and the Newcastle-Ottawa scale (NOS) score ranged from 5 to 9, indicating a moderate to high risk of methodological quality. Conclusion: The broad heterogeneity of the quality of studies limits the evidence of thermal ablation. Given these uncertainties, hepatic resection is currently the preferred approach.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".