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Record W2788938719 · doi:10.5539/cco.v7n1p30

Brain Tumor Treatment: 2017 Update

2018· article· en· W2788938719 on OpenAlexvenueno aff
Adri Chakraborty, Jennifer Han, Cristina Faltas, Natalia Coleman

Bibliographic record

VenueCancer and Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsBrain tumorMedicineCancer researchStem cellOncologyPopulationTumor cellsCancer stem cellCancerInternal medicinePathologyBiologyGenetics

Abstract

fetched live from OpenAlex

Malignant brain tumors are a heterogeneous group of diseases arising from different cell types that affect both adults and children. The high recurrence rate of malignant brain tumors typically is due to reappearance of focal masses, indicating that a sub population of tumor cells are insensitive to current therapies and may be responsible for reinitiating tumor growth. It is generally agreed that the resistant tumor cells are comprised of cancer stem cells or tumor-initiating cells. While brain tumor stem cells (BTSCs) were first isolated within the last decade, much of the early research has been focused on identifying the BTSC markers and therapeutic targets. The challenge however, is to translate this knowledge to therapeutics. In the current review, we survey the remedial strategies to target BTSCs, which includes diagnostic, pharmacologic, immunologic, viral, and post-transcriptional approaches.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.092
GPT teacher head0.471
Teacher spread0.379 · 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 teacher head, not a consensus.

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

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

Citations0
Published2018
Admission routes1
Has abstractyes

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