Assessment of the alignment between research funding allocation and consensus research priority areas in kidney cancer.
Bibliographic record
Abstract
e16538 Background: Finite resources are available to fund research, and it is important to ensure stakeholder input is identified and prioritized. In this light, the KCRNC and CIHR sponsored a consensus-based priority-setting partnership that brought together a group of patients, caregivers, and clinicians to identify the top 10 research priorities in kidney cancer (Table), with a consensus document published in 2017. The final step of the prioritization process was to determine how research funding allocation has aligned with these previously identified priority areas. We report the results of this assessment. Methods: We queried publicly available Canadian and American research databases to identify all research funds allocated to kidney cancer from 2018-2020. Each funded project was assessed to determine which priority areas were addressed. We evaluated the percent of projects and percent of funding dollars (converted to USD) allocated to priority areas. Projects were stratified by country, type of research (basic science/translational or clinical), and cancer stage of focus (localized and/or metastatic). Results: A total of 121 kidney cancer research projects were funded between 2018-2020, with 15 Canadian projects (total $ = 1,906,398 USD) and 106 American projects (total $ = 56,317,386 USD). Most projects were basic science or translational (88%). Half (50%) of the projects focused on localized cancer while 26% of projects focused on metastatic kidney cancer. Overall, 49% of projects aligned to one priority area, 47% of projects aligned to multiple priority areas, and 4% of projects were not aligned to priority areas. The priority areas which received the most funding were causes of kidney cancer (priority #10, 64% of funds), biomarkers (priorities #1b+1c+5, 59%), and immunotherapies (priority #4, 41%)(Table). Unfunded priority areas were supportive care (priority #6) and the role of biopsy in kidney cancer management (priority #8). Conclusions: Nearly all kidney cancer projects funded since 2018 were aligned with one or multiple stakeholder-identified research priority areas, although some priority areas remain underfunded. Mechanisms to improve distribution of funding to all priority areas may be warranted.[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 | 0.242 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".