Breast Cancer Subtypes and Response to Docetaxel in Node-Positive Breast Cancer: Use of an Immunohistochemical Definition in the BCIRG 001 Trial
Bibliographic record
Abstract
TO THE EDITOR: The article by Hugh et al 1 presents a useful classification of node-positive breast cancer to indicate survival and best use of chemotherapy with docetaxel, doxorubicin, and cyclophosphamide versus fluorouracil, doxorubicin, and cyclophosphamide. However, in the luminal B group—in which, compared with the luminal A group, survival was midway between the triple-negative and human epidermal growth factor receptor 2 (HER2) ‐positive groups—22% of the patients were HER2 positive. Because we routinely treat all HER2-positive patients with Herceptin (trastuzumab;Genentech,SouthSanFrancisco,CA)andchemotherapy,itwouldbeofgreatinteresttoseedisease-freeandoverallsurvival fortheluminalBgroupwithoutincludingtheHER2-positivepatients. The major difference between the luminal A and B groups without HER2 considered would be the Ki67-1 index, which was high in the majority of patients in the luminal B group and low in all patients in the luminal A group. Examining Figure 4 in the article, one has the impressionthatuseofdocetaxel,doxorubicin,andcyclophosphamide rendered the disease-free survival of the luminal B group without HER2-positive patients almost equivalent to that of the luminal A group. Thus, the major benefit of the luminal B subclassification may be in selecting patients who are most apt to benefit from taxanecontaining chemotherapy, which would then render their prognosis similar to that of the luminal A group.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| 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".