First Single-Centre Experience with the Novel HIF-α Inhibitor Belzutifan in Switzerland
Bibliographic record
Abstract
Belzutifan is a new HIF-α inhibitor mainly used in two different indications: von Hippel-Lindau syndrome-associated renal cell carcinoma, haemangioblastomas and pancreatic neuroendocrine tumours, as well as sporadic advanced pre-treated renal cell carcinoma. Although efficacy has been demonstrated in phase II and III studies, belzutifan is still not approved in many countries. In addition, von Hippel-Lindau syndrome is a rare disease. Therefore, there is virtually no real-world experience data of belzutifan efficacy available. We aim to determine the real-world efficacy and tolerability of belzutifan in patients with von Hippel-Lindau syndrome-associated tumours and in patients with sporadic advanced tyrosine kinase- and immune checkpoint inhibitors pre-treated for renal cell carcinoma. A retrospective analysis of five patients treated with belzutifan between 2023 and 2024 at a Swiss cancer centre was conducted. In this case series, all patients consistently benefitted from belzutifan with response to treatment. This case series provides real-world evidence that belzutifan is an effective and well-tolerated treatment option for patients with von Hippel-Lindau syndrome-associated renal cell carcinoma, haemangioblastomas and sporadic advanced pre-treated renal cell carcinoma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".