CN-14 * RETROSPECTIVE ANALYSIS OF ISCHEMIC CEREBRAL STROKES IN PATIENTS DIAGNOSED WITH A GLIOBLASTOMA DURING THE COURSE OF A BEVACIZUMAB TREATMENT
Bibliographic record
Abstract
Bevacizumab is an anti-vascular endothelial growth factor approved in the treatment of recurrent glioblastoma. It prolongs progression-free survival, improes radiologic response and contributes to reduce the dose of dexamethasone required to control peritumoral edema. Arterial and venous thromboembolic events represent significant toxicities related to the use of angiogenesis inhibitors. Various mechanisms could be implicated in bevacizumab-related strokes, as cardioembolic, lacunar stroke related to hypertension, deep venous thrombosis passing through a patent foramen ovale, pro-coagulant effect of the underlying glioblastoma and radiation-induced damages on peritumoral vessels. The characterization of the risk factors and stroke mechanisms is primordial since bevacizumab is currently used in the treatment of glioblastoma and VEGF receptor tyrosine-kinase inhibitors are actually in clinical trials for malignant gliomas. The prevention and optimal treatment of strokes depend on a precise description of the pathophysiological mechanisms involved. This retrospective study is performed with the collaboration of Roche to evaluate in detail risk factors and stroke mechanisms in the population of patients diagnosed with a glioblastoma who presented an ischemic cerebral stroke during the course of a bevacizumab treatment. The impact of the stroke on the overall survival and the location of the infarct in relation to the tumor emplacement will also be evaluated. This project will be completed in time for the SNO meeting, since we received the official approval from Roche and the ethics committee of Princess Margaret Hospital.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".