Bevacizumab use and central nervous system (CNS) hemorrhage.
Bibliographic record
Abstract
2073 Background: Bevacizumab is widely used and may cause life-threatening bleeding, usually at sites of disease involvement such as the CNS. We attempted to identify clinical characteristics associated with CNS hemorrhage in a broad population. Methods: We did a retrospective review of the FDA Medwatch database of adverse events reported with bevacizumab from 11/1997 to 5/2009. Results: We searched the database for keywords bleeding, hemorrhage, cerebral, intracranial, subarachnoid, cerebellar, hemorrhagic stroke, and brain. 17,466 reports were included in the database: 154 described CNS hemorrhage in 99 patients, and 1041 reports described non-CNS bleeds. For CNS bleeds, cerebral hemorrhage was the most frequent event reported (n=39), followed by intracranial hemorrhage (n=20) and subarachnoid hemorrhage (n=18). Median age was 62 years; 54% of patients were female. Primary cancer was colorectal (42%), glioma (13%), and breast cancer (10%). Patients received a median of three (1-36) doses of bevacizumab prior to the bleed. 54% of patients received myelosuppressive chemotherapy, and 30% had documented history of hypertension. Sixteen patients with CNS hemorrhage were reported to have CNS metastases. For 70 patients, information on CNS involvement was not reported. Death was reported as a complication of hemorrhage in 48% of patients. Nine patients with brain metastases died and CNS hemorrhage was the cause of death in seven. Six patients with glioma died, and CNS hemorrhage was the cause in three. One patient with brain metastases, and one patient with glioma also experienced a non-CNS bleed. Five patients in each group had received heparin, warfarin or NSAIDs. Low platelet counts were reported in 3 patients with CNS metastases and 2 patients with glioma. The most common factor associated with CNS hemorrhage was medication predisposing to bleeding, followed by thrombocytopenia. Hypertension, a risk factor for CNS hemorrhage, was reported in 4 patients with brain metastases, and 2 patients with glioma. Conclusions: In this database, 154 of 1195 reports of bleeding associated with bevacizumab described a CNS bleed. Although CNS bleeds were not common, they were the reported cause of death in more than half of the cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".