A prognostic factor (PF) index for overall survival in a HER2-negative endocrine-resistant metastatic breast cancer (MBC) population: Analysis from the ATHENA trial.
Bibliographic record
Abstract
555 Background: Chemotherapy is the standard of care for patients (pts) with HER2-negative endocrine-resistant MBC. The considerable variability in overall survival (OS) within this population relates essentially to prognostic factors (PF). Increasingly, large studies based on progression-free survival (PFS) as a primary endpoint are now being questioned. An accurate PF index may help in designing innovative trials with appropriate pts selection according to overall survival (OS) prognosis. Methods: The ATHENA trial assessed the safety of first-line bevacizumab combined with non-anthracycline-containing therapy in 2264 pts treated in 37 countries from 2006 to 2009. Pt characteristics, safety, and efficacy have been reported [Breast Cancer Res Treat 2011;130:133-43]. Sixty-one HER2-positive pts were excluded. A multivariate Cox regression model selected PF generating a simple PF index. Of note, skin, lymph node, ipsi-/contra- breast, or other soft tissue involvement was scored as a single organ. Results: After a median follow-up of 20.1 months and 1171 OS events (53% of pts), median OS for the entire sample and triple-negative (TNBC) and non-TNBC subgroups was 25.2 (95% CI 23.9–26.3), 18.3 (16.3–19.7) and 27.3 (26.3–29.3) months, respectively. PF most closely associated with poorer OS were: liver mets or >2 involved organs (HR 1.6; 95% CI 1.5–1.8); DFI ≤24 months (HR 1.7; 1.5–2.0); adjuvant anthracyline and/or taxane (HR 1.1; 1.2–1.4); and TNBC (HR 1.6; 1.4–1.8). A predictive model was designed stratifying by number of PF present (0/1 vs 2 vs 3/4). The model was consistent in both TNBC and non-TNBC populations (Table). Conclusions: A PF index may estimate figures and balance arms in future trials considering OS as primary objective. A well-defined group of non-TNBC accounting for 37% of patients has an OS estimate similar to the most aggressive TNBC. [Table: see text]
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".