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Record W4383482428 · doi:10.1158/2159-8290.cd-23-0495

Modeling the Landscape of Histone-Mutant Pediatric High-Grade Gliomas: A Study in Partner Alterations

2023· letter· en· W4383482428 on OpenAlexaff
Evan Lubanszky, Cynthia Hawkins

Bibliographic record

VenueCancer Discovery · 2023
Typeletter
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsHistoneMutantGliomaBiologyCancer researchMutationGeneticsComputational biologyGene

Abstract

fetched live from OpenAlex

SUMMARY: Pediatric high-grade gliomas represent a group of deadly, heterogeneous tumors, often driven by histone mutations and the accumulation of clonal mutations, correlating with different tumor types, locations, and age of onset. In this study, McNicholas and colleagues present 16 in vivo models of histone-driven gliomas to investigate subtype-specific tumor biology and treatment options. See related article by McNicholas et al., p. 1592 (7).

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 score0.620
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.336
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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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