Comparative analysis of medical costs after hepatectomy versus radiofrequency ablation in patients with hepatocellular carcinoma in real‐world clinical practice
Bibliographic record
Abstract
AIM: To compare the total medical costs and treatment outcomes in patients with primary hepatocellular carcinoma (HCC) according to their initial treatment, that is, hepatectomy or radiofrequency ablation (RFA), in real-world clinical practice in Japan. METHODS: This retrospective observational study was conducted using a medical claims database. Patients who underwent hepatectomy or RFA for primary HCC were matched using propensity score matching methods for available baseline characteristics. The average per-patient total medical costs from the date of initial treatment to up to 3 years were estimated. The 3-year survival and recurrence rates were estimated using the Kaplan-Meier method. RESULTS: Data of 1726 patients (863 in each group) were analyzed. The average 3-year medical costs were USD 8000 lower in the RFA group than in the hepatectomy group (USD 35,000 vs. USD 43,000). Patients in the RFA group had comparable 3-year overall survival to those in the hepatectomy group (87.6% vs. 90.4%). However, the 3-year recurrence rate was significantly higher in the RFA group than in the hepatectomy group (41.5% vs. 30.8%; hazard ratio = 1.56, 95% confidence interval: 1.31-1.87). CONCLUSIONS: In this 3-year study, patients achieved similar survival rates irrespective of initial treatment, but the RFA group had a lower total medical cost burden than the hepatectomy group. If both treatments are equally feasible, RFA may be a preferable initial curative treatment for primary HCC. However, careful consideration and adequate treatment should be given due to its higher recurrence risk.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".