Pazopanib may reduce bleeding in hereditary hemorrhagic telangiectasia
Bibliographic record
Abstract
Pazopanib (Votrient) is an orally administered tyrosine kinase inhibitor that blocks VEGF receptors potentially serving as anti-angiogenic treatment for hereditary hemorrhagic telangiectasia (HHT). We report a prospective, multi-center, open-label, dose-escalating study [50 mg, 100 mg, 200 mg, and 400 mg], designed as a proof-of-concept study to demonstrate efficacy of pazopanib on HHT-related bleeding, and to measure safety. Patients, recruited at 5 HHT Centers, required ≥ 2 Curacao criteria AND [anemia OR severe epistaxis with iron deficiency]. Co-primary outcomes, hemoglobin (Hgb) and epistaxis severity, were measured during and after treatment, and compared to baseline. Safety monitoring occurred every 1.5 weeks. Seven patients were treated with 50 mg pazopanib daily. Six/seven showed at least 50% decrease in epistaxis duration relative to baseline at some point during study; 3 showed at least 50% decrease in duration during Weeks 11 and 12. Six patients showed a decrease in ESS of > 0.71 (MID) relative to baseline at some point during study; 3/6 showed a sustained improvement. Four patients showed > 2 gm improvement in Hgb relative to baseline at one or more points during study. Health-related QOL scores improved on all SF-36 domains at Week 6 and/or Week 12, except general health (unchanged). There were 19 adverse events (AE) including one severe AE (elevated LFTs, withdrawn from dosing at 43 days); with no serious AE. In conclusion, we observed an improvement in Hgb and/or epistaxis in all treated patients. This occurred at a dose much lower than typically used for oncologic indications, with no serious AE. Further studies of pazopanib efficacy are warranted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".