TACE combined with hepatectomy in the treatment of primary hepatocellular carcinoma: A Meta analysis
Bibliographic record
Abstract
Objective To evaluate the therapeutic effects of preoperative prophylactic TACE for resectable primary hepatocellular carcinoma patients by mata analysis. Methods The research was conducted by retrieving China biomedical literature database, Chinese CNKI, VIP, Wanfang and PubMed, OVID, Embase, Cochrane library.Randomized controlled trials were evaluated by using the modified Jadad score and the case-control study were evaluated by using the Newcastle-Ottawa Scale respectively.All trials involved were analyzed by Stata12.0. Results 2 316 patients came from 3 randomized controlled trials (RCT) and 8 case-control articles including 752 patients in preoperative prophylactic TACE groups and 1 564 patients from liver resection only group.There was no significant difference in the operation time and blood loss between the two groups(operation time: SMD=0.058, 95% CI: -0.166-0.050, P=0.290; the amount of bleeding: SMD=-0.098, 95%CI: -0.204--0.08, P=0.070). The hospital stay was slightly prolonged in the preoperative prophylactic TACE groups (SMD=-0.86, 95%CI: -1.57--0.14, P=0.02). There was no significant difference between two groups in the 3-year overall survival and 5-year overall survival(the 3-year overall survival: RR=1.039, 95%CI: 0.964-1.121, P=0.314; the 5-year overall survival: RR=0.96, 95%CI: 0.86-1.08, P=0.505). Conclusion The Preoperative TACE fails to reduce the operation time and intraoperative blood loss, only prolonging the length of hospital stay. While the long-term survival rate remained unimproved. Key words: Carcinoma, hepatocellular; Hepatectomy; Survival rate; Meta-analysis
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".