A prognostic factor index for overall survival in patients receiving first-line chemotherapy for HER2-negative advanced breast cancer: An analysis of the ATHENA trial
Bibliographic record
Abstract
BACKGROUND: Evidence-based definitions of 'poor-prognosis' or 'aggressive' advanced breast cancer are lacking. PATIENTS AND METHODS: We developed a prognostic factor index using data from 2203 patients treated with first-line chemotherapy plus bevacizumab for HER2-negative advanced breast cancer. RESULTS: The risk factors most closely associated with worse OS were: disease-free interval ≤24 months; liver metastases or ≥3 involved organ sites; prior anthracycline and/or taxane therapy; triple-negative breast cancer (TNBC); and performance status 2 or prior analgesic/corticosteroid treatment. Risk of death was increased threefold in patients with ≥3 versus ≤1 risk factors (hazard ratio 3.0 [95% CI 2.6-3.4; p < 0.001]; median 16.0 vs 38.8 months, respectively). CONCLUSIONS: This prognostic index may enable identification of patients with a poorer prognosis in whom more intensive systemic regimens may be appropriate. The index may also be considered in designing new trials, although it requires validation in other datasets before extrapolation to non-bevacizumab-containing therapy. ClinicalTrials.gov identifier: NCT00448591.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".