A population-based comparison of treatment, resource utilization, and costs by cancer stage for Ontario patients with HER2-positive breast cancer
Bibliographic record
Abstract
PURPOSE: We sought to expand the currently limited, Canadian, population-based data on the characteristics, treatment pathways, and health care costs according to stage in patients with human epidermal growth factor receptor-2 positive (HER2+) breast cancer (BC). METHODS: We extracted data from the publicly funded health care system in Ontario. Baseline characteristics, treatment patterns, and health care costs were descriptively compared by cancer stage (I-III vs. IV) for adult women diagnosed with invasive HER2+ BC between 2012 and 2016. Resource use was multiplied by unit costs for publicly funded health care services to calculate costs. RESULTS: Overall, 4535 patients with stage I-III and 354 with stage IV HER2+ BC were identified. Most patients with stage I-III disease were treated with surgery (4372, 96.4%), with the majority having a lumpectomy, and 3521 (77.6%) received radiation. Neoadjuvant (NAT) and adjuvant (AT) systemic treatment rates were 20.1% (n = 920) and 88.8% (n = 3065), respectively. Systemic treatment was received by 311 patients (87.9%) with metastatic HER2+ BC, 264 of whom (84.9%) received trastuzumab. Annual health care costs per patient were nearly 3 times higher for stage IV vs. stage I-III HER2+ BC. CONCLUSION: Per-patient annual costs were substantially higher for women with metastatic HER2+ BC, despite less frequent exposure to surgery and radiation compared to those with early stage disease. Increasing NAT rates in early stage disease represent a critical opportunity to prevent recurrence and reduce the costs associated with treating metastatic HER2+ BC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".