Influence of HER2-low and HER2-zero status on pathologic complete response and survival in triple-negative breast cancer: a meta-analysis
Bibliographic record
Abstract
Objective To delve into the influence of different status of human epidermal growth factor receptor 2 (HER2) on the long-term survival of patients suffering from triple-negative breast cancer (TNBC), as well as the pathological complete response (pCR) following neoadjuvant therapy (NAT) via meta-analysis. Methods A computer search in the Embase, PubMed, Web of Science, and Cochrane Library databases was executed up to January 13, 2025, to collect studies related to HER2 status in TNBC patients. The articles were screened per the inclusion and exclusion criteria. The required data were extracted. The study quality was appraised by means of the Newcastle-Ottawa Scale, and statistical analysis was carried out utilizing Stata 15.0 software. Results 36 studies involving 54,277 patients with TNBC were included. According to the meta-analysis, the pCR rate after NAT was more notable in the HER2-zero group compared to the HER2-low group (RR = 0.90, 95%CI: 0.86-0.93, P < 0.001). Regarding overall survival (OS), HER2-low patients exhibited a better prognosis (HR = 0.93, 95%CI: 0.90-0.97, P < 0.001). For disease-free survival, breast cancer-specific survival, and recurrence-free survival, HER2-low patients might experience an enhanced prognosis. However, the results did not exhibit statistically significant. The sensitivity analysis confirmed the robustness of the meta-analysis results. No publication bias existed in studies on each outcome indicator. Conclusion HER2 status is essential for the prognostic assessment of TNBC patients, particularly in predicting pCR and OS outcomes. Systematic review registration https://www.crd.york.ac.uk/prospero/ , identifier PROSPERO CRD-420250642369.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.062 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".