Mini-invasive <i>vs</i> open resection of colorectal cancer and liver metastases: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: The safety and feasibility of the simultaneous resection of primary colorectal cancer (CRC) and synchronous colorectal liver metastases (SCRLM) have been demonstrated in some studies. Combined resection is expected to be the optimal strategy for patients with CRC and SCRLM. However, traditional laparotomy is traumatic, and the treatment outcome of minimally invasive surgery (MIS) is still obscure. AIM: To compare the treatment outcomes of MIS and open surgery (OS) for the simultaneous resection of CRC and SCRLM. METHODS: A systematic search through December 22, 2018 was conducted in electronic databases (PubMed, EMBASE, Web of Science, and Cochrane Library). All studies comparing the clinical outcomes of MIS and OS for patients with CRC and SCRLM were included by eligibility criteria. The meta-analysis was performed using Review Manager Software. The quality of the pooled study was assessed using the Newcastle-Ottawa scale. The publication bias was evaluated by a funnel plot and the Begg's and Egger's tests. Fixed- and random-effects models were applied according to heterogeneity. RESULTS: = 0.04). However, the operation time, rates and severity of overall complications, and rates of general complications showed no significant differences between the MIS and OS groups. Moreover, the overall survival and disease-free survival after MIS were equivalent to those after OS. CONCLUSION: Considering the studies included in this meta-analysis, MIS is a safe and effective alternative technique for the simultaneous resection of CRC and SCRLM. Compared with OS, MIS has less intraoperative blood loss and blood transfusion and quicker postoperative recovery. Furthermore, the two groups show equivalent long-term outcomes.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.060 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".