Chemotherapy and Delivery in the Treatment of Primary Brain Tumors
Bibliographic record
Abstract
Malignant astrocytomas are aggressive neoplasms with a dismal prognosis despite optimal treatment. Maximal resective surgery is traditionally complemented by radiation therapy. Chemotherapy is typically used on patients with tumor recurrence, when their functional status is congruent with further treatment. The classic agents used are nitrosoureas, but temozolomide is gradually taking a more prominent role recently. New agents, biological modifiers, are increasingly used in clinical trials in an effort to affect the intrinsic biologic aberrations harboured by tumor cells. These drugs comprise differentiation agents, anti-angiogenic agents, matrix-metalloproteinase inhibitors and signal transduction inhibitors, among others. The issue of chemotherapy delivery is also crucial. Classically, agents are administered either intravenously or orally. In an effort to circumvent the obstacle imposed by the blood-brain barrier, investigators are actively working on more invasive methods of delivery, namely intra-arterial infusion with or without blood-brain barrier disruption or direct intra-cerebral administration, with clysis or drug-impregnated wafers. This article reviews the standard cytotoxic agents that have been used to treat malignant astrocytomas, and the different combination regimen offering promise. In addition, recent advances with biological modifiers are also discussed, along with alternate methods to deliver the agents more efficiently across the blood-brain barrier.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".