Importance of dose intensity in neurooncology clinical trials: SummaryReport of the Sixth Annual Meeting of the Blood-Brain Barrier DisruptionConsortium
Bibliographic record
Abstract
Therapeutic options for the treatment of malignant brain tumors have been limited, in part, because of the presence of the blood-brain barrier. For this reason, the Sixth Annual Meeting of the Blood-Brain Barrier Disruption Consortium, the focus of which was the "Importance of Dose Intensity in Neuro-Oncology Clinical Trials," was convened in April 2000, at Government Camp, Mount Hood, Oregon. This meeting, which was supported by the National Cancer Institute, the National Institute of Neurological Disorders and Stroke, and the National Institute of Deafness and Other Communication Disorders, brought together clinicians and basic scientists from across the U.S. to discuss the role of dose intensity and enhanced chemotherapy delivery in the treatment of malignant brain tumors and to design multicenter clinical trials. Optimizing chemotherapy delivery to the CNS is crucial, particularly in view of recent progress identifying certain brain tumors as chemosensitive. The discovery that specific constellations of genetic alterations can predict which tumors are chemoresponsive, and can therefore more accurately predict prognosis, has important implications for delivery of intensive, effective chemotherapy regimens with acceptable toxicities. This report summarizes the discussions, future directions, and key questions regarding dose-intensive treatment of primary CNS lymphoma, CNS relapse of systemic non-Hodgkin's lymphoma, anaplastic oligodendroglioma, high-grade glioma, and metastatic cancer of the brain. The promising role of cytoenhancers and chemoprotectants as part of dose-intensive regimens for chemosensitive brain tumors and development of improved gene therapies for malignant gliomas are discussed.
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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 | 0.197 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".