Dual- versus single-agent HER2 inhibition and incidence of intracranial metastatic disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Observational studies have suggested that HER2 inhibition with trastuzumab may be associated with an increased incidence of intracranial metastatic disease (IMD) due to its ability to prolong survival. We hypothesized that prolonged survival associated with dual-agent HER2 inhibition may be associated with an even higher incidence of IMD. This study pooled estimates of IMD incidence and survival among patients with HER2-positive breast cancer receiving dual- versus single-agent HER2 targeted therapy, as well as trastuzumab versus chemotherapy, observation, or another HER2-targeted agent. We searched PubMed, EMBASE, and CENTRAL from inception to 25 March 2020. We included randomized controlled trials that reported IMD incidence for patients with HER2-positive breast cancer receiving trastuzumab as the experimental or control arm irrespective of disease stage. Among 465 records identified, 19 randomized controlled trials (32,572 patients) were included. Meta-analysis of four studies showed that dual HER2-targeted therapy was associated with improved overall survival (HR 0.76; 95% CI, 0.66-0.87) and progression-free survival (HR 0.77; 95% CI, 0.68-0.87) compared to single HER2-targeted therapy, but the risk of IMD was similar (RR 1.03; 95% CI, 0.83-1.27). Our study challenges the hypothesis that prolonged survival afforded by improved extracranial disease control is associated with increased IMD incidence.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".