Novel treatment strategies and key research priorities for patients with breast cancer and central nervous system (CNS) metastases
Bibliographic record
Abstract
Despite improvements in surgical techniques, advances in delivery of radiation therapy, and development of therapies with central nervous system (CNS) activity, the presence of CNS metastases from breast cancer is frequently associated with a poor prognosis. In 2023, the leadership of the Breast International Group and National Cancer Institute's National Clinical Trials Network convened a CNS working group to identify key challenges and discuss ways that international collaborations could push forward progress in the field. This review reflects initial discussions of the working group and addresses (1) the possible role of screening for CNS metastases, (2) optimal sequencing of local and systemic therapies among patients with human epidermal growth factor receptor 2 (HER2)-positive CNS metastases, (3) management of leptomeningeal disease, and (4) the importance of developing innovative clinical trials for treatment and prevention of CNS metastases across breast cancer subtypes that is informed by preclinical data/basic science, with seamless knowledge translation to allow for rapid clinical adoption.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".