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Tailoring Medulloblastoma Treatment Through Genomics: Making a Change, One Subgroup at a Time

2017· review· en· W2611192795 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Genomics and Human Genetics · 2017
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersHealth CanadaGarron Family Cancer CentreBrain Tumour Foundation of CanadaFondation Brain CanadaNational Cancer InstituteNational Institutes of HealthCureSearch for Children's Cancer
KeywordsMedulloblastomaGenomicsMedicineClinical trialDiseaseCompassionate UseOncologyBioinformaticsInternal medicineBiologyGenomeGeneticsCancer researchGene

Abstract

fetched live from OpenAlex

After more than a decade of genomic studies in medulloblastoma, the time has come to capitalize on the knowledge gained and use it to directly improve patient care. Although metastatic and relapsed disease remain poorly understood, much has changed in how we define medulloblastoma, and it has become evident that with conventional therapies, specific groups of patients are currently under- or overtreated. In this review, we summarize the latest insights into medulloblastoma biology, focusing on how genomics is affecting patient stratification, informing preclinical studies of targeted therapies, and shaping the new 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
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)0.0000.000

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 head0.144
GPT teacher head0.389
Teacher spread0.246 · 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