Bibliographic record
Abstract
394 Background: Prostate cancer (PCa) is the most prevalent malignancy in Canadian men; in 2023, an estimated 24,700 men will be diagnosed while ~4,700 will die of their disease. Lifetime risk of developing PCa is approximately 1-in-8 and approximately 3% of all deaths of Canadian men are caused by the disease. While Canada has a robust, modern health research landscape, with major academic centres located in all provinces and territories, the investment in academic PCa research – and the impact of that investment – has not been systematically quantified. An improved understanding of this landscape is necessary to ensure research funding remains efficient, impactful, and equitable. As such, we evaluated the size, scope, and impact of the investment in academic PCa research in Canada. Methods: We extracted funding records from the Canadian Research Information System (CRIS), the United States Congressionally Directed Medical Research Program database and the United States National Institutes of Health RePORT database for the National Cancer Institute, using keywords ‘prostate’ OR ‘prostatic’ AND ‘cancer’ OR ‘carcinoma’ for 1999-2021. US-sourced records were included when the principal investigator (PI) was based at a Canadian institution at the time the award was granted. Records were validated using data from the Canadian Cancer Research Alliance (CCRA). Intramural and industry-derived funding was unavailable and thus excluded from the analysis. Results: We identified 1,748 unique funding events (FEs) from 33 sources. These FEs involved 1,561 investigators and had an inflation-adjusted value of $682,113,116. The top three funders of PCa research were the Canadian Institutes of Health Research, Movember Canada, and the Canadian Cancer Society. Basic and translational research received ~83% of all funding while psychosocial, health economics, and epidemiology research received only ~7.2%. Strikingly, we found that 30.5% of all funding was held by 1% of investigators. We identified 6,671 dyads ( ie. pairs of collaborating investigators); 85% collaborated only once, while 15% collaborated at least twice (range: 2-19). Female investigators participated in significantly fewer collaborations than males (P: 3.28 x 10-3) and were less likely than expected to serve as PI (P: 8.87 x 10-8). FEs with ≥ one female PI had a significantly lower value than those with only male PIs (P: 1.34 x 10-6) and FEs with only female PIs had a significantly value than those with only male PIs (P: 7.32 x 10-4). Conclusions: While Canadian PCa research has been highly funded over the past 25 years, there remain substantial funding disparities across scientific disciplines, geographic regions, and, in particular, gender. There is also substantial ‘wealth inequality’; a small minority of investigators receive most of the funding. We are currently assessing stakeholder attitudes toward these disparities, to help inform the next phase of PCa research funding in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.048 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".