Comparison of Transarterial Bland and Chemoembolization for Neuroendocrine Tumours: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Treatment of hepatic metastases from neuroendocrine tumours improves survival and symptom relief. Hepatic arterial embolotherapy techniques include transarterial chemoembolization (TACE) and bland embolization (TAE). The relative efficacy of the techniques is controversial. The purpose of the present study was to use a meta-analysis and systematic review to compare tace with TAE in the treatment of hepatic metastases. Methods: A literature search identified studies comparing TACE and TAE for treatment of hepatic metastases. Outcomes of interest included overall survival (OS), progression-free survival (PFS), radiographic response, complications, and symptom control. The hazard ratios (HRs) and odds ratios (ORs) were estimated and pooled. Results: Eight studies and 504 patients were included. No statistically significant differences between TACE and TAE were observed for OS at 1, 2, and 5 years or for HRs [1-year OR: 0.72; 95% confidence interval (CI): 0.27 to 1.94; p < 0.52; 2-year OR: 0.69; 95% CI: 0.43 to 1.11; p < 0.12; 5-year OR: 0.91; 95% CI: 0.37 to 2.24; p < 0.85; HR: 0.96; 95% CI: 0.73 to 1.24; p < 0.74]. No statistically significant differences between TACE and TAE were observed for PFS at 1, 2, and 5 years or for HRs (1-year OR: 0.71; 95% CI: 0.38 to 1.55; p < 0.30; 2-year OR: 0.83; 95% CI: 0.33 to 2.06; p < 0.69; 5-year OR: 0. 91; 95% CI: 0.37 to 2.24; p < 0.85; HR: 0.99–1.74; 95% CI: 0.74 to 1.73; p < 0.97). Both techniques are safe and effective for symptom control. Conclusions: No statistically significant differences between TACE and TAE were observed for OS and PFS.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".