Global representativeness and impact of funding sources in cost-effectiveness research on systemic therapies for advanced breast cancer: A systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is the most incident tumor and, consequently, any new intervention can potentially promote a considerable budget impact if incorporated. Cost-effectiveness (CE) studies assist in the decision-making process but may be influenced by the country's perspective of analysis and pharmaceutical industry funding. METHODS: A systematic review of Medline, Scopus, and Web of Science from January 1st, 2012 to July 8th, 2022 was conducted to identify CE studies of tumor-targeted systemic-therapies for advanced BC. Articles without incremental cost-effectiveness ratio calculations were excluded. We extracted information on the country and class of drug studied, comparator type, authors' conflicts of interest (COI), pharmaceutical industry funding, and authors' conclusions. RESULTS: 71 studies comprising 204 CE assessments were included. The majority of studies were from the United States and Canada (44%), Asia (32%) and Europe (20%). Only 8% were from Latin America and none from Africa. 31% had pharmaceutical industry funding. The most studied drug classes were cyclin-dependent-kinase inhibitors (29%), anti-HER2 therapy (23%), anti-PD(L)1 (11%) and hormone therapy (11%). Overall, 34% of CE assessments had favorable conclusions. Pharmaceutical industry-funded articles had a higher proportion of at least one favorable conclusion (82% vs. 24%, p-value<0.001), European countries analyzed (45% vs. 9%, p-value = 0.003), and CE assessments with same class drug comparators (56% vs. 33%, p-value = 0.004). CONCLUSIONS: Breast cancer CE literature scarcely represents low-and-middle-income countries' perspectives and is influenced by pharmaceutical industry funding which targets European countries', frequently utilizes comparisons within same-drug class, and is more likely to have favorable conclusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".