A willingness-to-pay study of vascular endothelial growth factor inhibitors among patients with advanced non-small cell lung cancer
Bibliographic record
Abstract
6581 Background: Bevacizumab, a recombinant antibody which neutralizes vascular endothelial growth factor (VEGF), has been approved for the treatment of advanced non-small cell lung cancer (NSCLC). Costs of novel anticancer medications such as bevacizumab can be prohibitive for many lung cancer patients. This study sought to determine patients’ willingness-to pay (WTP) for bevacizumab and identify Canadian patient attitudes towards unfunded chemotherapeutic drugs. Methods: Participants attending outpatient lung cancer clinics at a major Canadian cancer center were given information pertaining to the risks and benefits of treatment with bevacizumab and were then asked about their WTP for this agent. Demographic and socioeconomic data were collected. A semi-structured interview was utilized to define the factors that limit patient access to unfunded chemotherapeutic agents. Results: 35 advanced NSCLC patients with a median age of 64.5 years and a median income of $40,000-$60,000 CAD consented to participate. Overall, participants were willing to pay a median of $75 CAD ($19-$250 CAD) per month for VEGF inhibitors, much less than the market cost of $8,000 CAD per month. 25 of 35 (71.4%) participants felt that the government should cover 100% of the drug cost, while 0%, 2.9%, 5.7%, and 20% of participants felt that the government should cover 0%, 25%, 50%, and 75% of the cost, respectively. Qualitative themes that arose included: (1) financial barriers are the primary obstacle in gaining access; (2) patients understand that the government cannot fund all novel cancer therapies; (3) patients are not willing to take loans or seek treatment outside of Canada without the prospect of cure; and (4) patients believe their oncologist will discuss all potential therapies, irrespective of access issues. Conclusions: Novel chemotherapeutic agents are unaffordable for the majority of NSCLC patients. Most patients would like the government to cover the cost of treatment, but many agree that the modest benefits of novel therapies may not always justify the large associated cost for government and publicly funded healthcare systems. [Table: see text]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".