Therapeutic outcome of combination therapy using immune-checkpoint inhibitors and tyrosine kinase inhibitors for metastatic non-clear-cell renal cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: We aimed to clarify the therapeutic outcome of combination therapy using immune-checkpoint inhibitors (ICIs) and/or tyrosine kinase inhibitors (TKIs) for meta-static non-clear-cell renal cell carcinoma (nccRCC). METHODS: We have been retrospectively investigating the therapeutic efficacy and prognosis in 36 patients with metastatic nccRCC undergoing combination therapy using two ICIs, ipilimumab plus nivolumab (ICI-ICI), and ICI plus TKI (ICI-TKI), at Kobe University and affiliated institutions since 2018. Progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and adverse event (AE) were compared. RESULTS: The first-line regimen was ICI-ICI in 26 cases and ICI-TKI in 10 cases. The ORRs in the ICI-ICI and ICI-TKI groups were 34.6 and 30.0%, respectively (p=0.9433). The 50% PFS for the ICI-TKI group was 9.7 months, significantly longer than that for the ICI-ICI group (4.6 months, p=0.0499), and there was no significant difference in OS between groups (p=0.3984). There was no significant difference in the occurrence rate of AE for below grade 2 (p=0.8535), nor above grade 3 (p=0.3786) between the ICI-ICI and ICI-TKI groups. CONCLUSIONS: From our analysis of real-world data, a better outcome of PFS was expected in the ICI-TKI group compared with that in the ICI-ICI group, while there was no significant difference in OS or ORR.
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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 | 0.000 |
| 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".