Faculty Opinions recommendation of VEGF inhibition and renal thrombotic microangiopathy.
Bibliographic record
Abstract
filtration barrier (Fig. 1A), occurs in 1 to 2% of bevacizumab-treated patients.1 Although potential causes of this type of proteinuria have been suggested, 2,3 it has been difficult to distinguish general (off-target) effects of therapy, such as an immunologic response to the monoclonal antibody, from direct (on-target) effects due to inhibition of endogenous VEGF signaling in noncancerous tissues.This report describes six patients with proteinuria and classic pathological features of thrombotic microangiopathy after bevacizumab therapy.The findings underscore the need for a better understanding of the renal consequences of VEGF inhibition.This topic has particular clinical relevance, given the impressive therapeutic potential of these drugs in a range of cancers and the expectation that an increasing number of patients will receive such agents in the future.We also provide direct experimental evidence of a mechanism of glomerular injury by VEGF inhibitors in a mouse model, showing that local genetic ablation of VEGF production in the kidney recapitulates the glomerular injury seen in our series of patients.The results support the concept that local production of VEGF plays a critical protective role in the pathogenesis of microangiopathic processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.050 |
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".