Establishing the Standard of Care for Patients with Newly Diagnosed and Recurrent Glioblastoma
Bibliographic record
Abstract
The current standard of care for patients with newly diagnosed glioblastoma includes maximal safe tumor resection followed by concurrent external-beam radiation with daily low-dose temozolomide followed by 6 to 12 months of adjuvant temozolomide, typically by using a cycle of 5 consecutive days out of 28. Efforts to improve on these results from the European Organisation for Research and Treatment of Cancer (EORTC)/National Cancer Institute of Canada (NCIC) trial using either dose-dense chemotherapy strategies or combinations with signal transduction modulators have, to date, been unsuccessful. Two large international randomized trials examining the efficacy of adding bevacizumab, an antiangiogenic agent, to the standard treatment have been completed, with expectations of results within in the next 2 years. For recurrent glioblastoma, there are no firmly established standards of care. Although intracavitary insertion of carmustine-impregnated polymers has been approved by the U.S. Food and Drug Administration (FDA), this strategy is not widely used. Bevacizumab has been FDA approved for recurrent glioblastoma, but no randomized trial has clearly demonstrated a survival benefit. Alternative dosing schedules of temozolomide (i.e., metronomic) has modest activity even in patients with prior temozolomide exposure. Clinical trials testing small-molecule signal transduction modulators have been disappointing, although most report a small response rate, suggesting that molecularly definable tumor subpopulations may help guide treatment decisions. Successful implementation of marker-based treatment would lead to personalized care and the creation of individualized standards of care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".