RTID-11. GBM AGILE: A GLOBAL, PHASE 2/3 ADAPTIVE PLATFORM TRIAL TO EVALUATE MULTIPLE REGIMENS IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Developing new therapies for patients with glioblastoma (GBM) requires focused interaction between industry, academia, nonprofits, patient advocacy, and health authorities, and novel approaches to clinical trials. GBM Adaptive Global Innovative Learning Environment (GBM AGILE) Trial was designed by over 130 global key opinion leaders in consultation with health authorities to provide an optimal mechanism for phase 2/3 development in GBM. The Sponsor of GBM AGILE is the Global Coalition for Adaptive Research, whose mission is to accelerate the development of treatments rare and deadly diseases by serving as sponsor of innovative trials. GBM AGILE is an international platform trial designed to evaluate multiple therapies in newly diagnosed and recurrent GBM. Its goals are to identify effective therapies for GBM and match effective therapies with patient subtypes, with data generated to support regulatory filing for new drug applications. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. The primary endpoint is overall survival. The trial is being conducted under a master Investigational New Drug Application/Clinical Trial Agreement and Master Protocol, allowing multiple drugs from different companies to be evaluated simultaneously and/or over time. The plan is to add experimental therapies as new information is identified and remove therapies as they complete their individual evaluation against a common control. GBM AGILE received IND approval from the FDA in April 2019, screening its first patient in June 2019. As of June 2020 over 200 patients have been screened. Expansion to Canada, Europe, China, and Australia is also underway. There is currently one investigational arm under evaluation in the trial, with two additional arms to be added in Q4 2020/ Q1 2021. Clinical trial information: NCT03970447.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".