Introduction: Glioblastoma: an update on pathophysiology and management strategies
Bibliographic record
Abstract
Glioblastoma multiforme remains an enigma for neu rooncology; our understanding has progressed, but the prognosis is still poor. In this issue of Neurosurgical Focus, the emphasis is on presenting current understanding of the pathophysiology of the disease and the manage ment strategies that stem from it. The issue can be divided into current management strategies and future treatments based on recent advances in understanding glioblastoma pathophysiology, including cancer stem cells, angiogen esis, and tumor heterogeneity. The first part of the issue is devoted to an evaluation of treatment methods available to patients today. From an operative standpoint, Hawasli et al. present a timely ar ticle on the use of stereotactic laser ablation of highgrade gliomas, a procedure that is available to most neurosur geons using the current armamentarium of imaging and ablative equipment. Lescher and colleagues assert that the time frame for the evaluation of postoperative chang es versus residual tumor is important in determining the extent of resection, given increasing data showing sur vival benefits with radiographically grossor near-total resections. From a medical perspective, Barbagallo et al. report data on longterm treatment with temozolomide, whereas Pompili et al. describe palliative care and end of life issues for patients with glioblastoma. The second part of this issue emphasizes the role of future treatments that will stem from recent advances in understanding the pathophysiology of glioblastoma mul tiforme. Sundar et al. present a timely review on the role of cancer stem cells in glioblastoma, which is followed by Khan and Ehtesham’s article on treatment based on targeting glioma stem cells. The importance and role of angiogenesis in glioblastoma pathophysiology is demon strated in Womeldorff et al.’s article on the role of hy poxiainducible factor1, hypoxia, and cytokineinduced angiogenesis. Understanding the role of angiogenesis leads to a better evaluation of the role of bevacizumab in glioblastoma treatment (Castro and Aghi) and the role of MET activation as a reaction to its use (Awad et al.). Tumor heterogeneity and the microenvironment are well recognized in glioblastoma, but recent advances in mo lecular genomics enable not only a precise diagnosis, but also prognostic implications. Aum et al. present a pre cise overview of the molecular and cellular heterogene ity found in glioblastoma. Golden et al. report preclinical data on the role of chloroquine and autophagy in glioblas toma. Two genes with important prognostic implications have recently been identified: IDH (Agnihotri et al.) and WT1 (Broaddus et al.). Identification of these targets can lead to new molecularly targeted therapies (Lau et al.). Treatment of glioblastoma requires a multidisciplin ary approach based on understanding the pathophysiol ogy of the disease, which will eventually lead to a better prognosis and quality of life for our patients. (http://thejns.org/doi/abs/10.3171/2014.9.FOCUS14677)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".