National Cancer Institute Breast Cancer Steering Committee Working Group Report on Meaningful and Appropriate End Points for Clinical Trials in Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE: To provide evidence-based consensus recommendations on choice of end points for clinical trials in metastatic breast cancer, with a focus on biologic subtype and line of therapy. METHODS: The National Cancer Institute Breast Cancer Steering Committee convened a working group of breast medical oncologists, patient advocates, biostatisticians, and liaisons from the Food and Drug Administration to conduct a detailed curated systematic review of the literature, including original reports, reviews, and meta-analyses, to determine the current landscape of therapeutic options, recent clinical trial data, and natural history of four biologic subtypes of breast cancer. Ongoing clinical trials for metastatic breast cancer in each subtype also were reviewed from ClinicalTrials.gov for planned primary end points. External input was obtained from the pharmaceutic/biotechnology industry, real-world clinical data specialists, experts in quality of life and patient-reported outcomes, and combined metrics for assessing magnitude of clinical benefit. RESULTS: The literature search yielded 146 publications to inform the recommendations from the working group. CONCLUSION: Recommendations for appropriate end points for metastatic breast cancer clinical trials focus on biologic subtype and line of therapy and the magnitude of absolute and relative gains that would represent meaningful clinical benefit.
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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 | 0.537 | 0.560 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.015 | 0.013 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".