Radiofrequency-Assisted Liver Resection Versus Clamp-Crush Liver Resection: Protocol for an Updated Meta-Analysis and Systematic Review
Bibliographic record
Abstract
BACKGROUND: Malignancy of the liver has historically meant a poor prognosis and remains the second most common cause of cancer-related deaths globally. Traditionally, hepatectomy has utilized the clamp-crush technique; however, this is associated with high incidence of postoperative complications. Many novel techniques have been developed-radiofrequency ablation and transarterial chemoembolization; however, these are not applicable to numerous cases. Clamp-crush liver resection (CCLR) remains the gold standard. Radiofrequency-assisted liver resection (RFLR) is a technique that aims to reduce mortality through bloodless liver resection. A systematic review was previously performed on RFLR but the results neither recommended nor refuted the use of RFLR owing to the lack of sufficient evidence from well-designed randomized controlled trials (RCTs) at the time. OBJECTIVE: The aim of the study is the meta-analysis and systematic review of recent studies comparing RFLR against CCLR. METHODS: Articles comparing RFLR and CCLR that were published from 2014 until 2019 will be reviewed and relevant data will be extracted and statistically analyzed through Review Manager 5 (by the Cochrane Collaboration) together with the results of the previous meta-analysis. RESULTS: Data collection is currently underway, with papers being screened. We hope to publish the results by the end of 2019. CONCLUSIONS: Given the high mortality rates currently associated with liver resection, it is imperative that novel surgical techniques are undertaken and investigated so we can improve best practice guidance and outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13437.
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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 | 0.034 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".