Expert consensus on the prevention of brain metastases in patients with HER2-positive breast cancer
Bibliographic record
Abstract
BACKGROUND: Patients with HER2-positive breast cancer have a significant risk of developing brain metastases (BrM), which have detrimental effects on survival outcomes and quality of life. Although there are several systemic treatment options available that may delay the appearance of BrM and secondary progression of previously treated BrM, there are still substantial unmet needs for this patient population and primary prevention remains elusive. METHODS: A group of experts created consensus statements, through a modified Delphi process, to bridge the gap between current unmet needs, available evidence, and international guidelines. RESULTS: The steering committee reviewed all relevant literature and formed research questions to be answered by the subsequent consensus statements. In total, 61 contributors provided feedback on the consensus statements, with 34 statements reaching agreement out of the 55 statements that were voted on altogether. Statements with consensus aimed to define BrM primary and secondary prevention, screening procedures, assessment of symptoms, treatment efficacy, and preventing the occurrence and progression of BrM, while acknowledging the possibilities and limitations in daily clinical practice. Some statements did not reach agreement for a variety of reasons, mostly due to lack of evidence. CONCLUSIONS: The consensus statements outlined in this publication provide a point of reference for daily clinical practice and can act as recommendations for clinical trial procedures and future guidelines.
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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 | 0.060 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".