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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.312 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".