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Record W2802181052 · doi:10.3340/jkns.2018.0061

Atypical Teratoid Rhabdoid Tumour : From Tumours to Therapies

2018· review· en· W2802181052 on OpenAlexafffund
Elizabeth A. Richardson, Ben Ho, Annie Huang

Bibliographic record

VenueJournal of Korean Neurosurgical Society · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Cancer Society
KeywordsSMARCB1SMARCA4MedicineEpigeneticsCancer researchEpigenetic therapyChemotherapyChromatin remodelingAtypical teratoid rhabdoid tumorStem cellRadiation therapyBioinformaticsPathologyOncologyDNA methylationBiologyInternal medicineGeneGeneticsMedulloblastomaGene expression

Abstract

fetched live from OpenAlex

Atypical teratoid rhabdoid tumours (ATRTs) are the most common malignant central nervous system tumours in children ≤1 year of age and represent approximately 1-2% of all pediatric brain tumours.ATRT is a primarily monogenic disease characterized by the bi-allelic loss of the SMARCB1 gene, which encodes the hSNF5 subunit of the SWI/SNF chromatin remodeling complex.Though conventional dose chemotherapy is not effective in most ATRT patients, high dose chemotherapy with autologous stem cell transplant, radiotherapy and/or intrathecal chemotherapy all show significant potential to improve patient survival.Recent epigenetic and transcriptional studies highlight three subgroups of ATRT, each with distinct clinical and molecular characteristics with corresponding therapeutic sensitivities, including epigenetic targeting, and inhibition of tyrosine kinases or growth/lineage specific pathways.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.314
Teacher spread0.285 · 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

Citations42
Published2018
Admission routes2
Has abstractyes

Explore more

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