Herceptin® (trastuzumab) in HER2-positive early breast cancer: a systematic review and cumulative network meta-analysis
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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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".