Using the 21‐gene assay from core needle biopsies to choose neoadjuvant therapy for breast cancer: A multicenter trial
Bibliographic record
Abstract
OBJECTIVE: 21-gene Recurrence Score (RS) could guide neoadjuvant systemic therapy (NST) to facilitate breast conserving surgery (BCS) for hormone receptor positive (HR+) breast cancers. METHODS: This study enrolled patients with HR+, HER2-negative, invasive breast cancers not suitable for BCS (size ≥ 2 cm). Core needle biopsy blocks were tested. For tumors with RS < 11, patients received hormonal therapy (NHT); patients with RS > 25 tumors received chemotherapy (NCT); patients with RS 11-25 were randomized to NHT or NCT. Primary endpoint was whether 1/3 or more of randomized patients refused assigned treatment. RESULTS: Sixty-four patients were enrolled. Of 33 patients with RS 11-25, 5 (15%) refused assignment to NCT. This was significantly lower than the 33% target (binomial test, P = 0.0292). Results for clinical outcomes (according to treatment received for 55 subjects) included successful BCS for 75% of tumors with RS < 11 receiving NHT, 72% for RS 11-25 receiving NHT, 64% for RS 11-25 receiving NCT, and 57% for RS > 25 receiving NCT. CONCLUSIONS: Using the RS to guide NST is feasible. These results suggest that for patients with RS < 25 NHT is a potentially effective strategy.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".