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Record W4389180044 · doi:10.1097/mph.0000000000002792

Bevacizumab in the Treatment of Refractory Brain Edema in High-grade Glioma

2023· article· en· W4389180044 on OpenAlexaff
Aysha Alsahlawi, Claudie Michaud‐Couture, Alexandre Lachance, Samuel Bergeron‐Gravel, Mélanie Létourneau, Catherine Bourget, Peter V. Gould, Panagiota Giannakouros, Emily M. Nakada, Damien Faury, Louis Crevier, Éric Bouffet, Nada Jabado, Valérie Larouche, Samuele Renzi

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreMcGill UniversityHospital for Sick ChildrenUniversité LavalSickKids FoundationMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineBevacizumabDexamethasoneRefractory (planetary science)GliomaBrain edemaEdemaCerebral edemaOncologyInternal medicineChemotherapyCancer research

Abstract

fetched live from OpenAlex

We report the case of a 14-year-old boy with a steroid-dependent refractory tumor whose longstanding dexamethasone treatment was successfully discontinued after a course of bevacizumab. The use of bevacizumab despite the absence of clear evidence of radionecrosis allowed a significant decrease in the amount of the brain edema.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.345
Teacher spread0.312 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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