PROGRESS IN CLINICAL NEUROSCIENCES: Advances in the Management of Low-Grade Gliomas
Bibliographic record
Abstract
The management of low-grade gliomas represents one of the most challenging and controversial areas in neuro-oncology. Many aspects of the treatment of low-grade gliomas are debated, including the optimal timing of surgery and radiotherapy, the benefit of extensive surgery, and the impact of these variables on the natural history of these indolent and generally incurable tumours. The recently published results of several large multicentre trials addressing the timing and dose of radiotherapy have provided solid evidence for delayed and reduced dose irradiation. These studies have also confirmed prognostic variables that can be used to guide management of individual patients. Among these variables is the observation that tumours with oligodendroglial features have a better natural history and response profile. The recognition that as many as two thirds of low-grade gliomas have oligodendroglial features, advances in molecular diagnostics making accurate pathologic diagnosis of oligodendroglial tumours possible, and the established chemosensitivity of malignant oligodendrogliomas, have raised new issues surrounding the potential value of chemotherapy for low-grade gliomas. This review will be restricted to low-grade diffuse astrocytomas, oligodendrogliomas, and low-grade mixed oligoastrocytomas in adults, and provide evidence-based guidelines for the management of these tumours, including the emerging role of chemotherapy as initial treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".