1282 Comparing Overall Survival and Oncological Recurrence in Laparoscopic and Open Hepatectomy for Colorectal Cancer Metastases: A Systematic Review
Bibliographic record
Abstract
Abstract Aim The use of laparoscopic surgery (LS) for liver resection has risen exponentially and is competing, in popularity, with open surgery (OPS). The aim of this study is to compare the oncological recurrence and overall survival of LS and OPS approaches for colorectal cancer liver metastases resection. Method A literature search was conducted, in-line with the PRISMA guidelines, via Ovid and PubMed to collect manuscripts dated between 01/01/2010-01/03/2021. The outcomes of interest were recurrence-free survival (RFS), overall survival (OS), and negative resection margins (R0). A modified quality assessment was achieved using the Newcastle-Ottawa Score (NOS) and the ROBINS-I Tool, and the data was analysed using Stata 16.0. Results Overall, 10 studies were selected of which included a total of 1924 patients. The results show the 1-, 3- and 5-years RFS and OS for LS and OPS were comparable, therefore not significant. Whereas the R0 differences were small yet significant - the LS group had an 89.9% R0 (95% CI: 87% to 92.8%) and for OPS was 85.4% (95% CI: 81% to 88.9%), Z – score of 1.6550, p = 0.0490 (p < 0.05). Even though the results are not significant for RFS and OS, LS is preferable for patients in improving the quality of life in the long-term. Conclusions There is little difference between OPS and LS methods as it pertains to the quality of life. With LS being less invasive and having marginally better results, doctors should consider prioritising LS for patients with colorectal cancer liver metastasis as an alternative to OPS.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".