EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer
Bibliographic record
Abstract
BACKGROUND: In early estrogen receptor (ER)-positive/HER2-negative breast cancer, the decision to administer chemotherapy is largely based on prognostic criteria. The combined molecular/clinical EndoPredict test (EPclin) has been validated to accurately assess prognosis in this population. In this study, the clinical relevance of EPclin in relation to well-established clinical guidelines is assessed. PATIENTS AND METHODS: We assigned risk groups to 1702 ER-positive/HER2-negative postmenopausal women from two large phase III trials treated only with endocrine therapy. Prognosis was assigned according to National Comprehensive Cancer Center Network-, German S3-, St Gallen guidelines and the EPclin. Prognostic groups were compared using the Kaplan-Meier survival analysis. RESULTS: After 10 years, absolute risk reductions (ARR) between the high- and low-risk groups ranged from 6.9% to 11.2% if assigned according to guidelines. It was at 18.7% for EPclin. EPclin reassigned 58%-61% of women classified as high-/intermediate-risk (according to clinical guidelines) to low risk. Women reclassified to low risk showed a 5% rate of distant metastasis at 10 years. CONCLUSION: The EPclin score is able to predict favorable prognosis in a majority of patients that clinical guidelines would assign to intermediate or high risk. EPclin may reduce the indications for chemotherapy in ER-positive postmenopausal women with a limited number of clinical risk factors.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".