Multi-institutional comparison of breast cancer risk stratification by 70-gene signature and 21-gene assay.
Bibliographic record
Abstract
522 Background: Breast cancer risk stratification with the 70-gene signature (70-GS) provides a binary low risk (LR) or high risk (HR) result; by contrast the 21-gene assay (21-GA) provides LR, intermediate (IR), and HR results. Results from these two assays were compared for 769 patients from 5 institutions. Methods: The study included patients from McGill University (n = 86), UPMC (n = 437), USF (n = 135), Morton Plant North Bay Hospital (n = 79), and Cleveland Clinic (n = 32, all 21-GA IR). Results: With the 70-GS, 487 (63%) patients had a LR and 282 (37%) patients had a HR result. Excluding 32 cases selected for 21-GA IR results (n = 737), the 21-GA gave 369 (50%), 250 (34%), and 118 (16%) patients with LR, IR, and HR scores, respectively. Using the TAILORx cutoff, there were 134 (18%), 432 (59%), and 171 (23%) patients with LR, IR, and HR scores, respectively. There were 329 (45%) and 486 (66%) patients who were not classified in the same risk category by both assays using the clinical and TAILORx cutoffs for IR, respectively. Conclusions: In a large multi-institutional study the 70-GS and 21-GA results were discordant in 45-66% of patients, and the proportion of patients with a 21-GA score in the IR range varied from 34-59%. The 70-GS provided clinically actionable results for all patients. [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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".