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Record W2804206785

Pediatric Medulloblastomas: Classification, Pathophysiology and Therapeutics

2018· article· en· W2804206785 on OpenAlexaffvenue
James Cairns

Bibliographic record

VenueUBC Faculty of Medicine medical journal · 2018
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedulloblastomaMedicineEtiologyPosterior fossaFourth ventricleBioinformaticsPathophysiologyWnt signaling pathwayPosterior cranial fossaPathogenesisPopulationPathologyOncologyBiologyGeneGenetics
DOInot available

Abstract

fetched live from OpenAlex

Medulloblastomas are a type of primary malignant brain tumour arising within the cerebellum and posterior cranial fossa adjacent to the fourth ventricle. Medulloblastomas are the most common primary malignant brain tumours in the pediatric population and an increasing body of basic and clinical research is providing important insights into the etiology, pathogenesis and development of novel therapeutics to treat these highly invasive tumours. Recent advances in genomics and transcriptomics have allowed researchers to classify and diagnose medulloblastomas based on differences in genetic and transcriptome factors. Based on these findings medulloblastomas have been classified into four main subgroups including the: 1) Wnt subgroup, 2) Shh subgroup, 3) Group 3 tumours and 4) Group 4 tumours. These advancements in classifying and diagnosing medulloblastomas are important as different tumour subgroups have different pathophysiology, differing prognoses and variable responses to treatment. This article will briefly highlight the latest classification criteria of pediatric medulloblastoma, review molecular and genetic features believed to be involved in the pathogenesis of each of the 4 subgroups of medulloblastoma and provide an overview of treatments and therapies that are currently available and in development for medulloblastoma.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.337
Teacher spread0.298 · 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.

Study designObservational
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 routes2
Has abstractyes

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