Survival in patients with non-metastatic breast cancer treated with adjuvant trastuzumab in clinical practice
Bibliographic record
Abstract
PURPOSE: The NSABP Trial B-31 and NCCTG Trial N9831 (B-31/N9831 trials, Romond et al. in N Engl J Med 353:1673-84, 2005. doi:10.1056/NEJMoa052122; Perez et al. in J Clin Oncol 32:3744-52, 2014. doi:10.1200/JCO.2014.55.5730) established the efficacy of adjuvant trastuzumab for patients with HER2-positive early stage breast cancer. We aimed to estimate the overall survival (OS) and relapse-free survival (RFS) of HER2-positive non-metastatic breast cancer patients treated with adjuvant trastuzumab in a clinical practice setting in the United States. METHODS: Adult women initiating adjuvant trastuzumab within 1 year of breast cancer surgery were identified in the health claims database of the US Department of Defense (01/2003-12/2012). OS and RFS unadjusted rates at 4 and 6 years after the first trastuzumab treatment following the breast cancer diagnosis were estimated from Kaplan-Meier analyses. RESULTS: The study sample included 3188 women followed for a median of 3.3 years after trastuzumab initiation and treated continuously with trastuzumab for a median of 12 months. The OS rates (95 % confidence intervals) at 4 and 6 years were 90.0 % (88.6-91.2) and 87.1 (85.3-88.6), respectively. The corresponding RFS rates were 75.8 % (74.0-77.5) and 72.7 (70.7-74.7), respectively. The OS and RFS rates at 6 years reported in the B-31/N9831 trials were 89.8 and 81.4 %, respectively. CONCLUSIONS: OS rates estimated in this study were in range with those estimated in the B-31/N9831 trials, while RFS rates were lower. However, patients in the B-31/N9831 trials were younger and possibly had fewer comorbidities than patients in the current study; these differences were not adjusted for in the crude OS and RFS analyses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".