Intracranial metastases from solid tumors: Call to action and consensus from the Society for Neuro-Oncology and American Society of Clinical Oncology collaborative
Bibliographic record
Abstract
Intracranial metastases (ICM), specifically parenchymal brain metastases, remain a major clinical challenge in solid tumor oncology, despite recent advances in cancer therapies which have led to improvements in survival for these patients. Improving outcomes even further in this patient population will require a multi-disciplinary approach, including pre-clinical and translational studies, clinical trials, and studies of patient reported outcomes and quality of life. At the 2023 and 2024 joint Society for Neuro-Oncology (SNO) and American Society of Clinical Oncology (ASCO) CNS Metastases Conferences, two ICM collaborative group think tanks convened, composed of diverse, multi-disciplinary stakeholders, including basic and translational researchers, clinical trialists, and clinicians from academia and the community setting. Here we summarize the key knowledge gaps and consensus recommendations put forth by these two think tanks. Advances in ICM research and improvements in patient outcomes will require close inter-specialty and inter-institutional collaboration between stakeholders, including pre-clinical and translational researchers, clinical investigators, industry, and regulatory bodies.
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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 | 0.180 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.033 | 0.060 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".