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Record W2100289020 · doi:10.3171/2014.9.focus14677

Introduction: Glioblastoma: an update on pathophysiology and management strategies

2014· article· en· W2100289020 on OpenAlexaff
Mitchel S. Berger, Jeffrey N. Bruce, Thomas C. Chen, Gelareh Zadeh

Bibliographic record

VenueNeurosurgical FOCUS · 2014
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlioblastomaPathophysiologyMedicineNeuroscienceComputer sciencePsychologyPathologyCancer research

Abstract

fetched live from OpenAlex

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)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.004

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.

Opus teacher head0.007
GPT teacher head0.246
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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