Efficacy and safety of epirubicin applied in transcatheter arterial chemoembolization for hepatocellular carcinoma
Bibliographic record
Abstract
OBJECTIVES: This study was aimed to evaluate the efficacy and safety of epirubicin applied in transcatheter arterial chemoembolization (TACE) for the treatment of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Studies were searched in Embase, PubMed, and Springer until August 10, 2016. All the studies were screened with inclusion and exclusion criteria. The quality assessment of the eligible studies was performed with the Newcastle-Ottawa Scale and the Jadad Scale. Response rate, recurrence, mortality, and thrombocytopenia were evaluated with risk ratios (RRs) with 95% confidence intervals (CIs). The heterogeneity and publication bias were assessed. RESULTS: Ten eligible studies were included with a total of 993 objects. The data were extracted and summarized. The overall results were calculated including response rate (RR = 0.98, 95% CI: 0.83-1.15), recurrence (RR = 0.75, 95% CI: 0.58-0.96), mortality (RR = 0.71, 95% CI: 0.39-1.28), and thrombocytopenia (RR = 0.42, 95% CI: 0.09-1.93), without significant heterogeneity. There was a significant heterogeneity for mortality; thus, the random effects model was used. No publication bias was observed in this study. CONCLUSIONS: The results of meta-analysis indicated that epirubicin applied in TACE has an obvious efficacy for the treatment of HCC, with significantly decreased recurrence while without superiority of safety.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".