Response Assessment in Neuro-Oncology (RANO): more than imaging criteria for malignant glioma
Bibliographic record
Abstract
The introduction of antiangiogenic therapies for the treatment of malignant glioma and the effect of these agents on standard imaging studies were the stimuli for forming a small group of investigators to critically evaluate the limitations of the Macdonald criteria in assessing response to treatment. The initial goal of this group was to highlight the challenges in accurately determining the efficacy of therapeutic interventions for malignant glioma and to develop new criteria that could be implemented in clinical care as well as in the design and conduct of clinical trials. This initial Response Assessment in Neuro-Oncology (RANO) effort started in 2008 and over the last 7 years, it has expanded to include a critical review of response assessment across several tumor types as well as endpoint selection and trial design to improve outcome criteria for neuro-oncological trials. In this paper, we review the overarching principles of the RANO initiative and the efforts to date. We also highlight the diverse and expanding efforts of the multidisciplinary groups of investigators who have volunteered their time as part of this endeavor.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".