Characteristics of patients with longer treatment period of lenvatinib for unresectable hepatocellular carcinoma: A post-hoc analysis of post-marketing surveillance study in Japan
Bibliographic record
Abstract
Patient profiles suitable for long-term lenvatinib treatment for unresectable hepatocellular carcinoma (uHCC) are yet to be fully understood. This post-hoc analysis aimed to identify such patient characteristics and explore the impact of treatment duration and relative dose intensity (RDI) on treatment outcomes. The data were obtained from 703 patients in a multicenter, prospective cohort study in Japan. Lenvatinib-naïve patients with uHCC were enrolled between July 2018 and January 2019 and were followed up for 12 months. Moreover, patients were dichotomized using the median treatment duration into the longer- (≥177 days; n = 352) or shorter-treatment (<177 days; n = 351) groups. The longer-treatment group often had better performance status, lower Child-Pugh score and better modified albumin-bilirubin grade than the shorter treatment group (p<0.05 for all). The objective response rate (47.6% vs. 28.2%; p<0.001) and disease control rate (92.4% vs. 60.2%; p<0.001) were both significantly higher in the longer-treatment groups than in the shorter-treatment groups. The proportion of patients with any adverse drug reactions was generally similar between the two treatment groups. Within the longer-treatment group, the disease control rate was high regardless of dose modification (i.e., RDI <100% vs. ≥100% during the initial 177 days) (91.2% vs. 98.0%). In conclusion, patients with longer treatment tended to have better overall conditions. Lenvatinib dose modifications at the physician's discretion, considering the balance between effectiveness and safety, may contribute to the long-term treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".