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Record W2898608732 · doi:10.1093/annonc/mdy273.395

Phase II open-label, global study evaluating dabrafenib in combination with trametinib in pediatric patients with BRAF V600–mutant high-grade glioma (HGG) or low-grade glioma (LGG)

2018· article· en· W2898608732 on OpenAlexaff
Darren Hargrave, Olaf Witt, Kenneth J. Cohen, Roger J. Packer, Andrej Lissat, Uwe Kordes, Theodore W. Laetsch, Lindsey M. Hoffman, Álvaro Lassaletta, Nicolas U. Gerber, Stephen W. Gilheeney, Stefan Holm, Christof M. Kramm, David Sumerauer, C. Reitmann, Mark W. Russo, Éric Bouffet

Bibliographic record

VenueAnnals of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsTrametinibDabrafenibMedicineMEK inhibitorOncologyInternal medicineCohortGliomaMelanomaPhases of clinical researchClinical trialCancer researchCancerMAPK/ERK pathwayMetastatic melanomaKinaseVemurafenibBiology

Abstract

fetched live from OpenAlex

Background: Activation of the MAPK pathway via the BRAF V600 mutation has been observed in several tumors. This mutation is observed in a subset of pediatric brain tumors, including HGG and LGG for which limited therapeutic options are currently available. In a phase I/II clinical trial of pediatric patients (pts) with recurrent or refractory BRAF V600–mutant relapsed tumors, the BRAF inhibitor dabrafenib demonstrated its efficacy, including complete responses, in pediatric pts with HGG and LGG.

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)0.002
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.100
GPT teacher head0.436
Teacher spread0.337 · 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 designNon-randomized trial
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

Citations7
Published2018
Admission routes1
Has abstractyes

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