Can routinely collected administrative data effectively be used to evaluate and validate endpoints used in breast cancer clinical trials? Protocol for a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) are a critical component of evidence-based medicine and the evolution of patient care. However, the costs of conducting a RCT can be prohibitive. A promising approach toward reduction of costs and lessening of the burden of intensive and lengthy patient follow-up is the use of routinely collected healthcare data (RCHD), commonly called real-world data. We propose a scoping review to identify existing RCHD case definitions of breast cancer progression and survival and their diagnostic performance. METHODS: We will search MEDLINE, EMBASE, and CINAHL to identify primary studies of women with either early-stage or metastatic breast cancer, managed with established therapies, that evaluated the diagnostic accuracy of one or more RCHD-based case definitions or algorithms of disease progression (i.e., recurrence, progression-free survival, disease-free survival, or invasive disease-free survival) or survival (i.e., breast-cancer-free survival or overall survival) compared with a reference standard measure (e.g., chart review or a clinical trial dataset). Study characteristics and descriptions of algorithms will be extracted along with measures of the diagnostic accuracy of each algorithm (e.g., sensitivity, specificity, positive predictive value, negative predictive value), which will be summarized both descriptively and in structured figures/tables. DISCUSSION: Findings from this scoping review will be clinically meaningful for breast cancer researchers globally. Identification of feasible and accurate strategies to measure patient-important outcomes will potentially reduce RCT budgets as well as lessen the burden of intensive trial follow-up on patients. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework ( https://doi.org/10.17605/OSF.IO/6D9RS ).
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How this classification was reachedexpand
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".