Herceptin® (trastuzumab) in HER2-positive early breast cancer: a systematic review and cumulative network meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Originator trastuzumab (Herceptin®; H) is an antibody-targeted therapy to treat patients with human epidermal growth factor receptor 2-positive (HER2+) early breast cancer (EBC). We investigated the overall survival (OS) advantage conferred by the addition of H to chemotherapy for HER2+ EBC patients and how the OS advantage changed over time. METHODS: A systematic literature review (SLR) identified randomized controlled trials (RCTs) and non-randomized studies (NRSs) published from January 1, 1990 to January 19, 2017, comparing systemic therapies used in the neoadjuvant/adjuvant settings to treat HER2+ EBC patients. Bayesian cumulative network meta-analyses (cNMAs) of OS were conducted to assess the published literature over time. Heterogeneity was assessed through sensitivity and subgroup analyses. RESULTS: The SLR identified 31 unique studies (28 RCTs, 3 NRSs) included in the OS analyses from 2008 to 2016. In the reference case cNMA (RCTs alone), initial evidence demonstrated an OS advantage for H/chemotherapy compared with chemotherapy alone in HER2+ EBC patients. As additional OS data were published, the precision around this survival benefit strengthened over time. Both H/anthracycline-containing chemotherapy and H/non-anthracycline-containing chemotherapy regimens provided similar OS advantages for HER2+ EBC patients. CONCLUSION: This analysis represents the most comprehensive SLR/cNMA to date of published OS data in HER2+ EBC studies. These findings demonstrate why H/chemotherapy is now the established standard of care in HER2+ EBC. In the case of H, the benefits of early patient access far outweighed the risk of waiting for more precise information. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017055763.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.068 | 0.009 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it