Application of Novel Response/Progression Measures for Surgically Delivered Therapies for Gliomas
Bibliographic record
Abstract
BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) Working Group is an international, multidisciplinary effort to develop new standardized response criteria for clinical trials in brain tumors. The RANO group identified knowledge gaps relating to the definitions of tumor response and progression after the use of surgical or surgically based treatments. OBJECTIVE: To outline a proposal for new response and progression criteria for the assessment of the effects of surgery and surgically delivered therapies for patients with gliomas. METHODS: The Surgery Working Group of RANO identified surgically related end-point evaluation problems that were not addressed in the original Macdonald criteria, performed an extensive literature review, and used a consensus-building process to develop recommendations for how to address these issues in the setting of clinical trials. RESULTS: Recommendations were formulated for surgically related issues, including imaging changes associated with surgical resection or surgically mediated adjuvant local therapies, the determination of progression in the setting where all enhancing tumor has been removed, and how new enhancement should be interpreted in the setting where local therapies that are known to produce nonspecific enhancement have been used. Additionally, the terminology used to describe the completeness of surgical resections has been recognized to be inconsistently applied to enhancing vs nonenhancing tumors, and a new set of descriptors is proposed. CONCLUSION: The RANO process is intended to produce end-point criteria for clinical trials that take into account the effects of prior and ongoing therapies. The RANO criteria will continue to evolve as new therapies and technologies are introduced into clinical trial and/or practice.
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 | 0.201 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".