Intravenous bevacizumab for complications of hereditary hemorrhagic telangiectasia: a review of the literature
Bibliographic record
Abstract
BACKGROUND: Hereditary hemorrhagic telangiectasia (HHT) is a multisystem disease that is marked by mutations regulating vasculature formation. Epistaxis is the most commonly reported symptom, but gastrointestinal bleeding, anemia, hepatic issues, and pulmonary disease are also common. There is a growing body of evidence in the literature concerning using the monoclonal antibody against vascular endothelial growth factor (VEGF), bevacizumab, in patients with HHT. This treatment is gaining support for managing HHT because it directly inhibits the VEGF proteins that can be elevated as a result of the HHT mutations. We reviewed the current literature on the outcomes from intravenous bevacizumab treatment for HHT with a focus on epistaxis outcomes. METHODS: A systematic review of the literature was performed using Ovid MEDLINE, Scopus, and Cochrane databases. English citations, both national and international, were reviewed and filtered for relevance. RESULTS: Eighteen studies were included in this review. The majority of citations were case reports. All studies reported improvements. Specifically, 14 reported improvements in epistaxis, and 11 reported hemoglobin improvement following intravenous (IV) bevacizumab. Lack of uniformity in data presentation prevented a meta-analysis. CONCLUSION: This is the first systematic review analyzing the data involving HHT patients treated with bevacizumab. The results show that patients treated with bevacizumab have global improvements as well as specific improvements in hemoglobin levels. Although all of the studies reported improvements, there are several limitations, including inconsistencies in outcome reporting. A large, randomized, controlled study is needed to further investigate hemorrhage and epistaxis outcomes in HHT patients treated with intravenous bevacizumab.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".