Elemene injection combined with transcatheter arterial chemoembolization for unresectable hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: To evaluate the short-term efficacy, long-term efficacy, and adverse events (AEs) of elemene plus transcatheter arterial chemoembolization (TACE) in comparison with TACE alone for the treatment of hepatocellular carcinoma (HCC). METHODS: PubMed, EMBASE, the Cochrane Library, the Chinese Scientific Journal Full-text Database, Wanfang Data, CBM, and VIP were searched by 2 reviewers using the same search strategy for clinical studies on elemene plus TACE in the treatment of HCC. These articles were screened according to pre-established inclusion and exclusion criteria, and the qualities of the included studies were assessed using the Newcastle-Ottawa scale. The primary outcomes were the objective response rate (ORR), the 1-year survival rate and AEs. Review Manager 5.3 and Stata 15.0 were used for the meta-analysis. RESULTS: A total of 10 studies involving 543 patients (TACE + elemene = 277, TACE alone = 266) were included. The results showed that the ORR was significantly improved in the combined treatment group compared to the TACE alone group (odds ratio [OR] = 2.72, 95% confidence interval [CI]: 1.84-4.00, P < .05). TACE + elemene significantly increased the 1-year survival rate (OR = 2.79, 95% CI: 1.58-4.95, P < .05). We also found no significant difference in gastrointestinal reactions (OR = 0.97, 95% CI: 0.57-1.64, P = .90), fever (OR = 0.80, 95% CI: 0.37-1.71, P = .56), or bone marrow suppression (OR = 0.73, 95% CI: 0.44-1.22, P = .23) between the 2 groups. CONCLUSION: Based on current findings, TACE + elemene injection may improve the ORR and the 1-year survival rate for HCC patients compared to TACE alone. Arterial perfusion may be superior to intravenous guttae.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".