Insights From Mixture Cure Modeling of Molecular Markers for Prognosis in Breast Cancer
Bibliographic record
Abstract
With the ultimate aim of improving clinical management of breast cancer, investigators have sought to identify molecular genetic markers that stratify newly diagnosed patients into subtypes differing in short- or long-term prognosis. Conventional survival models can fail to describe adequately the relationship between subtype and disease recurrence, particularly when there is a substantial proportion of long-term disease-free survivors. The observed patterns of disease-free survival in an undifferentiated patient cohort may be explained by an underlying mixture of two subgroups: patients who will remain free of disease in the long term (ie, cured), and those who will experience disease recurrence within their lifetime (ie, susceptible.) In this article, we review the concepts and methods of the mixture cure models and apply them in the analysis of molecular genetic prognostic factors for disease-free survival and time to disease recurrence in a cohort of patients with axillary lymph node-negative breast cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".