Response Assessment in Neuro-Oncology (RANO) 2009–2025: Broad scope and implementation—A progress report
Bibliographic record
Abstract
Since its first activities in 2008 and 2009, the Response Assessment in NeuroOncology (RANO) group has given guidance on response assessment, trial design, and trial procedures to improve and standardize the way clinical trials in neurooncological studies are performed. To achieve its objectives, a variety of working groups have been initiated that cover many aspects of clinical trial design and outcome assessment in patients with tumors affecting the Central Nervous System. The RANO working groups are built on expertise without a formal structure, which makes rapid responses to new developments possible. RANO is aiming at evidence-based guidelines and recommendations, but in the absence of evidence will provide consensus-based guidance achieved by inviting recognized international experts. In its 15 years of existence, more than 60 RANO papers have been published mostly in high-ranking journals, and its recommendations have been accepted by regulators and industry as guiding principles. RANO organizes two meetings per year, one in conjunction with the annual American Society for Clinical Oncology (ASCO) meeting, and one during the annual Society for Neuro-Oncology meeting. These meetings are open, as are the working groups of RANO. New initiatives are welcomed.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".