Discrepancies Between Canadian Cancer Research Funding and Site-Specific Cancer Burden: A Spotlight on Ten Disease Sites
Bibliographic record
Abstract
Background: Cancer research is essential in evaluating the safety and effectiveness of emerging cancer treatments, which in turn can lead to ground-breaking advancements in cancer care. Given limited research funding, allocating resources in alignment with societal burden is essential. However, evidence shows that such alignment does not typically occur. The objective of the present study was to provide an updated overview of site-specific cancer research investment in Canada and to explore potential discrepancies between the site-specific burden and the level of research investment. Methods: The 10 cancer sites with the highest mortality in 2015-which included brain, female breast, colorectal, leukemia, lung, non-Hodgkin lymphoma, ovary, pancreas, prostate, and uterus-were selected for the analysis. Information about site-specific research investment and cancer burden (raw incidence and mortality) was obtained from the Canadian Cancer Research Survey and Statistics Canada's cansim (the Canadian Socio-Economic Information Management System) respectively. The ratio of site-specific research investment to site-specific burden was used as an indicator of overfunding (ratio > 1) or underfunding (ratio < 1). Results: The 3 cancer sites with the highest research investments were leukemia, prostate, and breast, which together represented 51.3% of 2015 cancer research funding. Conversely, the 3 cancer sites with the lowest investments were uterus, pancreas, and ovary, which together represented 7.8% of 2015 research funding. Relative to site-specific cancer burden, the lung, uterus, and colorectal sites were consistently the most underfunded. Conclusions: Observed discrepancies between cancer burden and research investment indicate that some cancer sites (such as lung, colorectal, and uterus) seem to be underfunded when site-specific incidence and mortality are taken into consideration.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.028 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".