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Record W1574065726 · doi:10.1177/0883073815599259

Low Grade Gliomas in Children

2015· review· en· W1574065726 on OpenAlexafffund
Alan Chalil, Vijay Ramaswamy

Bibliographic record

VenueJournal of Child Neurology · 2015
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsPilocytic astrocytomaGliomaMedicineClinical trialRisk stratificationOncologyAstrocytomaInternal medicineCancer research

Abstract

fetched live from OpenAlex

Gliomas represent the most common solid tumor of the nervous system, and can occur as both low and high-grade tumors. Current risk stratification and treatment approaches rely heavily on the morphological classification of gliomas whereby low-grade gliomas have an excellent prognosis, particularly pilocytic astrocytomas, while high-grade gliomas have a poor prognosis. The past decade has witnessed a dramatic increase in scholars' knowledge of the biology of pediatric low-grade gliomas particularly through the advent of integrated genomics and next generation sequencing. Indeed, many of these biological advances are changing treatment paradigms, particularly in low-grade gliomas, where rationale targeted therapies are currently being explored in clinical trials. In this review the authors summarize the current approach to pediatric low grade gliomas and outline the biological advances over the past 10 years, which will be driving the next generation of clinical trials.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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 designOther design
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

Citations56
Published2015
Admission routes2
Has abstractyes

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