Association between the burden of disease and research funding by the Medical Research Council of Canada and the National Institutes of Health. A cross-sectional study.
Bibliographic record
Abstract
BACKGROUND: The Medical Research Council of Canada (MRCC) is the major Canadian agency responsible for funding biomedical health research in this country. Disease-specific funding by the United States National Institutes of Health (NIH) has been studied and is not independent of burden-of-disease parameters. We tested the association between disease-specific MRCC funding, disease-specific NIH funding and various burden-of-disease parameters. METHOD: Information on 1994/99 MRCC funding was obtained from the MRCC database for 29 diseases. NIH funding and burden-of-disease counterparts for the year 1996 were gleaned from a recent publication. The association between data series was measured by correlation coefficients. RESULTS: Disease-specific incidence, mortality and years-of-life lost did not correlate significantly with 1994/99 disease-specific MRCC funding but prevalence (r = 0.54, p = 0.005) and disability-adjusted life-years did (r = 0.48, p = 0.009). A correlation coefficient of 0.50 (p = 0.006) was calculated between 1996 NIH funding and 1996/97 MRCC funding. Two disease categories, cirrhosis and alcohol abuse, received a greater percentage of funds from the NIH than from the MRCC. Two other disease categories, epilepsy and perinatal disease, received a greater percentage of funds from the MRCC than from the NIH. CONCLUSIONS: Disease-specific MRCC grants in the past 5 years correlated with 2 of the usual burden-of-disease parameters: disability-adjusted life-years and disease prevalence. A statistically-significant correlation was observed between disease-specific grants awarded by the MRCC and the NIH.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".