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Record W4386537738 · doi:10.1093/neuonc/noad137.263

P11.29.B UPDATE ON GBM AGILE: A GLOBAL, PHASE 2/3 ADAPTIVE PLATFORM TRIAL TO EVALUATE MULTIPLE REGIMENS IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA

2023· article· en· W4386537738 on OpenAlexaffabout
Michael Weller, Brian M. Alexander, Donald A. Berry, N Blondin, Meredith Buxton, Webster K. Cavenee, Howard Colman, John de Groot, M de la Fuente, François Ducray, Benjamin M. Ellingson, Gary Gordon, Emma Maria Viktoria Hyddmark, Mustafa Khasraw, Andrew B. Lassman, E Lee, Michael Lim, Ingo K. Mellinghoff, Tom Mikkelsen, James Perry, Erik P. Sulman, Kirk Tanner, Patrick Y. Wen, Antje Wick, Alfred Yung, Timothy F. Cloughesy

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAgile software developmentMedicineBiomarkerGlioblastomaRandomizationClinical trialOncologyInternal medicineMedical physicsComputer scienceCancer researchBiologySoftware engineering

Abstract

fetched live from OpenAlex

Abstract BACKGROUND GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 platform trial designed to rapidly identify experimental therapies that improve overall survival and confirm efficacious experimental therapies and associated biomarker signatures to support new drug approvals and registration. GBM AGILE is a collaboration between academic investigators, patient organizations and industry to support new drug applications for newly diagnosed (ND) and recurrent GBM. METHODS The primary objective of GBM AGILE is to identify therapies that effectively improve the overall survival in patients with ND or recurrent GBM. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. New experimental therapies are added as information about promising new drugs is identified, while therapies are removed as they complete their evaluation. GBM AGILE has screened over 1400 patients and enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/site/month. There are 41 active sites in the US, 4 active sites in Canada and 3 active sites in Europe with a total of 15 sites planned for Switzerland, France and Germany. Expansion to Australia is currently underway. GBM AGILE operates under a Master Protocol which allows multiple drugs from different pharmaceutical/biotech companies to be evaluated simultaneously and/or over time against a common control. Along with an adaptive trial design, shared control arm and operational processes to serve the goal of helping patients receive optimal care in a fast and efficient manner, GBM AGILE incorporates new design and operational elements to enhance efficiencies, including more recently dose finding and enhanced safety management components. The dose finding phase allows for an initial evaluation of the experimental study drug in combination with radiotherapy and temozolomide, and/or lomustine in a limited number of patients at a select number of study sites within the trial in order to ensure that there are no critical safety signals before expansion to a larger subset of patients for enhanced safety monitoring followed by broader inclusion of the combination at all global study sites. The investigational drugs that have employed the dose finding phase and enhanced safety monitoring process have tolerable safety profile with toxicities that are monitorable, reversible, and not related to the control arm treatments. Through the use of improved and flexible processes, GBM AGILE continues to serve as a global trial that supports the efficient and rapid incorporation and evaluation of new experimental therapies for patients with GBM. Clinical trial information: NCT03970447.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.056
GPT teacher head0.370
Teacher spread0.314 · 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 designRandomized 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

Citations0
Published2023
Admission routes2
Has abstractyes

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