Postoperativer TACE in hepatocellular carcinoma: a Meta-analysis
Bibliographic record
Abstract
Objective To evaluate the therapeutic effects of postoperative TACE (transarterial chemoembolization) in patients with hepatocellular carcinoma after liver resection. Methods The search was conducted on China biomedical literature database, Chinese CNKI, VIP, Wanfang and PubMed, OVID, Embase and Cochrane library. All the literatures were searched till the end of January 2016. The quality of the included studies were evaluated using the modified Jadad score for randomized controlled trials and the Newcastle-Ottawa Scale for case controlled studies. The trials were analyzed by Stata12.0. Results A total of 2184 patients from 1 randomized controlled trial (RCT) and 14 case-control studies were included. These patients were divided into two groups based on the treatment method. 469 patients were included into the hepatic resection only group and 1087 patients were into the postoperative TACE group. The results showed that postoperative TACE improved the 1-year survival for hepatocellular carcinoma. Subgroup analyses were conducted on the risk factor group (tumor diameter greater than 5cm; multiple nodules and blood vessel invasion) and the no risk factor group. Postoperative TACE played an important role in the risk factor group (subgroup analysis for the no risk factor group: RR=1.10, 95%CI: 0.97, 1.25, P>0.05; subgroup analysis for the risk factor group: RR=1.16, 95%CI: 1.02, 1.32, P 0.05). Conclusions Postoperative TACE prolonged the survival rate of patients with hepatocellular carcinoma, especially those who had the following risk factors: tumor diameter greater than 5 cm; multiple nodules; blood vessel invasion. Postoperative TACE can be recommended as a routine treatment. Key words: Primary hepatocellular carcinoma; Liver resection; Transarterial chemoembolization (TACE); Meta-analysis; Curative effect
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".