Proposal for Standardized Definitions for Efficacy End Points in Adjuvant Breast Cancer Trials: The STEEP System
Bibliographic record
Abstract
PURPOSE: Standardized definitions of breast cancer clinical trial end points must be adopted to permit the consistent interpretation and analysis of breast cancer clinical trials and to facilitate cross-trial comparisons and meta-analyses. Standardizing terms will allow for uniformity in data collection across studies, which will optimize clinical trial utility and efficiency. A given end point term (eg, overall survival) used in a breast cancer trial should always encompass the same set of events (eg, death attributable to breast cancer, death attributable to cause other than breast cancer, death from unknown cause), and, in turn, each event within that end point should be commonly defined across end points and studies. METHODS: A panel of experts in breast cancer clinical trials representing medical oncology, biostatistics, and correlative science convened to formulate standard definitions and address the confusion that nonstandard definitions of widely used end point terms for a breast cancer clinical trial can generate. We propose standard definitions for efficacy end points and events in early-stage adjuvant breast cancer clinical trials. In some cases, it is expected that the standard end points may not address a specific trial question, so that modified or customized end points would need to be prospectively defined and consistently used. CONCLUSION: The use of the proposed common end point definitions will facilitate interpretation of trial outcomes. This approach may be adopted to develop standard outcome definitions for use in trials involving other cancer sites.
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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 | 0.285 | 0.418 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".