Four Cycles of Docetaxel-Cyclophosphamide versus Anthracycline-Taxane as Adjuvant Chemotherapy for HER2-Negative, Axillary Lymph Node Negative Breast Cancer: A Real-World Comparison of Alberta Patients Treated 2008–2012
Bibliographic record
Abstract
Uncertainty exists around the need to include an anthracycline if taxane-based adjuvant chemotherapy is being used for human epidermal growth factor receptor-2 (HER2) negative and axillary lymph node negative (LNN) breast cancer. We identified all patients who were diagnosed with HER2-negative, LNN breast cancer treated with docetaxel-cyclophosphamide for four cycles (DC4) or an anthracycline-taxane (AT) regimen following surgical resection in Alberta from 2008 through 2012. We used propensity score methods to match each patient treated with AT to up to four patients treated with DC4 on potentially confounding clinicopathologic and treatment variables. We compared the 10-year invasive disease free survival (iDFS), breast cancer specific-survival (BCSS) and overall survival (OS) and assessed the effect of the type of adjuvant chemotherapy on these outcomes using Cox regression. Of the 726 eligible patients, 657 (90.5%) were treated with DC4 and 69 (9.5%) were treated with AT. Matching created a group of 202 women treated with DC4 and eliminated differences in clinicopathologic and treatment factors. There was no statistically significant difference for the treatment effects of matched DC4 patients compared to the AT patients on iDFS (75.7% vs. 76.8%, p = 0.75; hazard ratio (HR) = 1.05, 95% CI = 0.65 to 1.8), BCSS (88.1% vs. 87%, p = 0.8; HR = 0.91, 95% CI = 0.42 to 1.9), or OS (87.1% vs. 86.9%, p= 0.96; HR = 0.98, 95% CI = 0.46 to 2.1). Four cycles of DC as compared with an AT regimen yielded similar 10-year iDFS, BCSS and OS amongst patients with HER2-negative, LNN breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".