Treatment Patterns and Health Outcomes among Patients with HER2 IHC0/-Low Metastatic or Recurrent Breast Cancer
Bibliographic record
Abstract
Improved understanding of the biological heterogeneity of breast cancer (BC) has facilitated the development of more effective and personalized approaches to treatment. This study describes real-world evidence on treatment patterns and outcomes for a population-based cohort of patients with human epidermal growth factor receptor (HER2) IHC0 and -low BC with de novo or recurrent disease from Alberta, Canada. Patients 18+ years old diagnosed with HER2 IHC0/-low, de novo/recurrent BC from 2010 to 2019 were identified using Alberta's cancer registry. Analyses of these patients' existing electronic medical records and administrative claims data were conducted to examine patient characteristics, treatment patterns, and survival outcomes. A total of 3413 patients were included in the study, of which 72.10% initiated first line hormonal and non-hormonal systemic therapy. The 1-year overall survival (OS) was 81.09% [95% CI, 79.52-82.69]. Recurrent patients had a higher OS compared to de novo patients: 54.30 months [95% CI, 47.80-61.90] vs. 31.5 months [95% CI, 28.40-35.90], respectively. Median OS was 43.4 months [95% CI, 40.70-47.10] and 35.80 months [95% CI, 29.00-41.70] among patients with HER2-low and HER2 IHC0 cancer, respectively. The study results provide real-world evidence regarding the clinical outcomes of HER2 IHC0/-low and de novo/recurrent disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".