Changing Paradigms—An Update on the Multidisciplinary Management of Malignant Glioma
Bibliographic record
Abstract
Treatment of malignant glioma requires a multidisciplinary team. Treatment includes surgery, radiotherapy, and chemotherapy. Recently developed agents have demonstrated activity against recurrent malignant glioma and efficacy if given concurrently with radiotherapy in the upfront setting. Oligodendroglioma with 1p/19q deletions has been recognized as a distinct pathologic entity with particular sensitivity to radiotherapy and chemotherapy. Randomized trials have shown that early neoadjuvant or adjuvant administration of procarbazine, lomustine, and vincristine chemotherapy prolongs disease-free survival; however, it has no impact on overall survival. Temozolomide, a novel alkylating agent, has shown modest activity against recurrent glioma. In combination with radiotherapy in newly diagnosed patients with glioblastoma, temozolomide significantly prolongs survival. Molecular studies have demonstrated that the benefit is mainly observed in patients whose tumors have a methylated methylguanine methyltransferase gene promoter and are thus unable to repair some of the chemotherapy-induced DNA damage. For lower-grade glioma, the use of chemotherapy remains limited to recurrent disease, and first-line administration is the subject of ongoing clinical trials. Irinotecan and agents like gefitinib, erlotinib, and imatinib targeting the epidermal growth factor receptor and platelet-derived growth factor receptor have shown some promise in recurrent malignant glioma. This review summarizes recent developments, focusing on the clinical management of patients in daily neuro-oncology practice.
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".