Treatment patterns and medical costs after hepatectomy in real‐world practice for patients with hepatocellular carcinoma in Japan
Bibliographic record
Abstract
AIM: To examine the treatment patterns and medical costs in real-world practice among patients who received hepatectomy for hepatocellular carcinoma (HCC) in Japan. METHODS: Data of patients who underwent hepatectomy as an initial therapy for primary HCC were extracted from a Japanese medical claims database from April 2008 to December 2019. The types of additional treatments for recurrent HCC and medical costs for up to 3 years from the first hepatectomy were analyzed. The average cumulative cost per patient starting on the date of the first hepatectomy was calculated using the Kaplan-Meier sample-average method. RESULTS: Data from 2 342 patients (median age, 71 years) were analyzed. Overall, 35.6% of patients received at least one HCC treatment within 3 years of the first hepatectomy. The total average cumulative 3-years medical cost was JPY 4 993 300 (95% confidence interval [CI]: 4 804 100 to 5 220 500). Surgical procedures were the most costly components in the first month after hepatectomy, whereas the costs of drugs, which mainly included antiviral and antineoplastic medications, increased thereafter. Patients with advanced stage HCC, hepatitis C, or a higher Charlson Comorbidity Index at hepatectomy, or those who required additional treatment, especially with antineoplastic drugs for recurrent HCC, incurred higher medical costs. CONCLUSIONS: Patients with HCC after hepatectomy experienced a large economic burden, which was more serious for those with advanced stage HCC, higher comorbidities, and hepatitis at baseline and for patients treated with antineoplastic drugs. A treatment selection that considers its medical cost burden would help to reduce some of these economic burdens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".