Proposal for Standardized Definitions for Efficacy End Points in Adjuvant Breast Cancer Trials: The STEEP System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.186 | 0.558 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it