Survival outcome of lobar or segmental transcatheter arterial embolization with ethanol-lipiodol mixture in treating hepatocellular carcinoma
Bibliographic record
Abstract
AIM: To evaluate the clinical outcome and cost-effectiveness of transcatheter arterial ethanol-lipiodol embolotherapy on hepatocellular carcinoma (HCC). METHODS: One hundred patients with HCC who were treated only by lobar or segmental transarterial embolization (TAE) with ethanol-lipiodol mixture were enrolled in this study. The 1st- and 2nd-year survival rates were analyzed to evaluate the feasibility of its method. These outcomes of our patients were individually correlated to the Child-Pugh classification and the computed tomographic features of HCC. RESULTS: The overall 1st- and 2nd-year survival rates were 72% and 46%, respectively. The patients were classified into three groups according to their liver function status: 68 patients as Child-Pugh class A, 26 as Child B, and 6 as Child C. Child A had better survival rate than the Child B and/or C. The 1st-year survival rates of patients with Child A-C were 84%, 50%, and 33.3% respectively and the 2nd-year survival rates were 55.5%, 28.5%, and 33.3%, respectively. According to the computed tomographic features, solitary HCC with maximum diameter less than 5 cm had the best outcome with the 1st-year survival rate of 100% and the 2nd-year survival rate of 71.4%, while solitary HCC with maximum diameter over 5 cm and multiple HCC had the 1st-year survival rates of 75% and 63.7%, respectively, and the 2nd-year survival rates of 33.3% and 44.4%, respectively. Only one patient was complicated with abscess formation and was cured with antibiotic therapy. No mortality resulted from the procedures performed. CONCLUSION: TAE with ethanol-lipiodol mixture is an economic, safe and feasible method for treating HCC, especially for the patients with smaller solitary HCC or with liver function status of Child-Pugh class A.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".