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Record W4315926803 · doi:10.1159/000529099

Advances and Clinical Trials Update in the Treatment of Diffuse Intrinsic Pontine Gliomas

2023· review· en· W4315926803 on OpenAlexaff
Cecilia Dalle Ore, Christina Coleman, Nalin Gupta, Sabine Mueller

Bibliographic record

VenuePediatric Neurosurgery · 2023
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsMedicinePersonalized medicineClinical trialGliomaBiopsyPrecision medicineOncologyModalitiesInternal medicinePathologyBioinformaticsCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Diffuse intrinsic pontine gliomas (DIPGs) are high-grade gliomas (HGGs) that occur primarily in children, and represent a leading cause of death in pediatric patients with brain tumors with a median overall survival of only 8-11 months. SUMMARY: While these lesions were previously thought to behave similarly to adult HGG, emerging data have demonstrated that DIPG is a biologically distinct entity from adult HGG frequently driven by mutations in the histone genes H3.3 and H3.1 not found in adult glioma. While biopsy of DIPG was historically felt to confer unacceptable risk of morbidity and mortality, multiple studies have demonstrated that stereotactic biopsy of DIPG is safe, allowing not only for improved understanding of DIPG but also forming the basis for protocols for personalized medicine in DIPG. However, current options for personalized medicine in DIPG are limited by the lack of efficacious targeted therapies for the mutations commonly found in DIPG. Multiple treatment modalities including targeted therapies, immunotherapy, convection-enhanced delivery, and focused ultrasound are in various stages of investigation. KEY MESSAGE: Increasing frequency of biopsy for DIPG has identified distinct driving mutations that may serve as therapeutic targets. Novel treatment modalities are under investigation.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
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 head0.186
GPT teacher head0.448
Teacher spread0.262 · 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 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

Citations15
Published2023
Admission routes1
Has abstractyes

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