Efficacy of First-Line Treatment With Pertuzumab and Trastuzumab in Advanced Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer in Routine Clinical Practice
Bibliographic record
Abstract
Background: The first-line treatment for human epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC) involves a combination of trastuzumab, pertuzumab, and a taxane (TPH). This study assessed the efficacy of trastuzumab and pertuzumab (PH) in routine practice, following the treatment protocols of Uruguay's National Resources Fund (FNR), akin to clinical trials. Methods: Patients with advanced MBC treated with PH between 2008 and 2022 per FNR protocols were evaluated. The Kaplan-Meyer method and log-rank test were utilized for analyzing overall survival (OS). Demographic and clinical variables, including age, menopausal status, and hormone receptors (HR), were analyzed. Results: The study included 318 PH-treated patients. The median age was 56 years, with 63.2% being postmenopausal and 60.4% HR and HER-2 positive. With a median follow-up of 17.2 months, the median OS was 29 months. OS varied based on HR status and the presence of metastases at different sites, significantly lower in patients with brain, cutaneous/subcutaneous, and pulmonary metastases. Additionally, OS was higher in patients treated at private institutions compared to public ones. Conclusions: This study demonstrates the disparity in oncological treatment efficacy between clinical trials and clinical reality in Uruguay, emphasizing the importance of authentic environment research for more representative and effective medicine in Latin America.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".