Bladder cancer research funding in Canada and the United States: A comparison between stakeholder priorities and resource allocation from 2017 to 2019.
Bibliographic record
Abstract
422 Background: There is an increasing emphasis on patient-centered research to encourage cancer care that is responsive to patients' needs. Previously, the Bladder Cancer Advocacy Network (BCAN) Patient Survey Network (PSN) engaged patients and other key stakeholders and compiled a prioritized list of research questions in bladder cancer. However, it is uncertain whether these priorities have successfully guided subsequent resource allocation by funding agencies. The purpose of this study was to understand how bladder cancer research funding has been allocated in recent years and to determine whether funding patterns have aligned with patient and caregiver priorities. Methods: We investigated publicly available research databases online or contacted agencies directly to determine bladder cancer research fund allocation in Canada and the US from 2017 to 2019. Each funding competition and all funded projects were evaluated to assess whether they aligned with previously identified priority research areas. Trends in funding allocation were assessed and several key variables including country, year, agency focus, cancer stage, and funding amount were analyzed. Results: Fifteen agencies provided funding to bladder cancer research between 2017 and 2019, amounting to a total of $78,525,974 in funding for 298 projects across Canada and the US. Of this funding, $23,268,258 (30%) went towards projects addressing the stakeholder-identified high priority research questions, $15,575,064 (20%) went towards projects addressing lesser priority questions, and the remaining $39,682,652 (50%) funded projects addressing questions which did not align with previously identified stakeholder priorities. General agencies (non-bladder cancer-specific) funded more priority (high and lesser) projects than bladder cancer-specific agencies (p < 0.001). Among projects addressing non-muscle invasive bladder cancer, 36% of funding went to high priority areas, compared to 13% and 27% for muscle-invasive bladder cancer and metastatic bladder cancer, respectively. Among the top 10% of projects (n = 30) with the greatest funding amount (combined $43,249,792), 45% of the funding went to high priority areas, 21% went to lesser priority areas, and 34% went to non-priority areas. Conclusions: Of nearly $80,000,000 USD allocated to bladder cancer research in recent years, approximately half was allocated to projects addressing stakeholder-identified priority areas while half was allocated to projects that were not aligned with stakeholder priorities. More work is needed to ensure stronger alignment between stakeholder-identified priority areas and funding allocation in bladder cancer research.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".