Quality-adjusted life years for HER2-positive, early-stage breast cancer using trastuzumab-containing regimens in the context of cost-effectiveness studies: a systematic review
Bibliographic record
Abstract
INTRODUCTION: This study aims to provide a comprehensive assessment of economic and health-related quality of life (HRQoL) outcomes for human epidermal growth factor receptor 2 (HER2)-positive, early-stage breast cancer patients treated with trastuzumab-containing regimens, by focusing on both Incremental Cost-Effectiveness Ratios (ICERs) and quality-adjusted life years (QALYs). METHODS: A systematic search was conducted across PubMed, Embase, and Scopus databases without language or publication year restrictions. Two independent reviewers screened eligible studies, extracted data, and assessed methodology and reporting quality using the Drummond checklist and Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022), respectively. Costs were converted to US dollars (US$) for 2023 for cross-study comparison. RESULTS: Twenty-two articles, primarily from high-income countries (HICs), were included, with ICERs ranging from US$13,176/QALY to US$254,510/QALY, falling within country-specific cost-effectiveness thresholds. A notable association was observed between higher QALYs and lower ICERs, indicating a favorable cost-effectiveness and health outcome relationship. EQ-5D was the most utilized instrument for assessing health state utility values, with diverse targeted populations. CONCLUSIONS: Studies reporting higher QALYs tend to have lower ICERs, indicating a positive relationship between cost-effectiveness and health outcomes. However, challenges such as methodological heterogeneity and transparency in utility valuation persist, underscoring the need for standardized guidelines and collaborative efforts among stakeholders. REGISTRATION: PROSPERO ID: CRD42021259826.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".