Cerebrovascular Ischemic Events in Patients With Advanced Renal Cell Carcinoma Treated With Anti-Angiogenic Tyrosine Kinase Inhibitors: A Report on Two Cases With Different Outcomes
Bibliographic record
Abstract
Small-molecule tyrosine kinase inhibitors (TKIs), targeting tumor angiogenesis, have revolutionized the treatment of advanced renal cell carcinoma (RCC) over the last decade. Their rationale is that most clear cell RCCs have alterations in the Von Hippel-Lindau (VHL) gene pathway that leads to over-expression of pro-angiogenic factors such as vascular endothelial growth factor and platelet-derived growth factor that drive tumor growth and dissemination. The toxicity profile of these therapies, whilst generally mild and manageable, is quite distinct from that of conventional cytotoxic chemotherapy and immunotherapy. Arterial thrombotic events have been reported with pazopanib and axitinib (1-2% incidence) and patients with recent vascular events have been excluded from phase III trials of these drugs in RCC. We report two cases of cerebral infarction likely related to these treatments in female patients without any history of macrovascular disease or any conventional risk factors such as hyperlipidemia, hypertension or diabetes mellitus. Treatment-related arterial thromboembolism may develop rapidly and unpredictably in these patients and consideration should be given to aggressively monitoring and modifying any pre-existing vascular risk factors. Older patients with risk factors for vascular disease or with a prior history of such events must have an informed discussion regarding the risks and benefits of treatment. It remains to be seen whether prophylactic anti-platelet therapies such as aspirin, dipyridamole or clopidogrel might reduce the risk of stroke in these patients with an acceptable risk of bleeding. J Med Cases. 2015;6(10):463-467 doi: http://dx.doi.org/10.14740/jmc2289w
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".