Report of National Brain Tumor Society roundtable workshop on innovating brain tumor clinical trials: building on lessons learned from COVID-19 experience
Bibliographic record
Abstract
On July 24, 2020, a workshop sponsored by the National Brain Tumor Society was held on innovating brain tumor clinical trials based on lessons learned from the COVID-19 experience. Various stakeholders from the brain tumor community participated including the US Food and Drug Administration (FDA), academic and community clinicians, researchers, industry, clinical research organizations, patients and patient advocates, and representatives from the Society for Neuro-Oncology and the National Cancer Institute. This report summarizes the workshop and proposes ways to incorporate lessons learned from COVID-19 to brain tumor clinical trials including the increased use of telemedicine and decentralized trial models as opportunities for practical innovation with potential long-term impact on clinical trial design and implementation.
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 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 | 0.181 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".