Research Output and the Public Health Burden of Cancer: Is There Any Relationship?
Bibliographic record
Abstract
PURPOSE: The relative distribution of research output across cancer sites is not well described. Here, we evaluate whether the volume of published research is proportional to the public health burden of individual cancers. We also explore whether research output is proportional to research funding. METHODS: Statistics from the Canadian and American cancer societies were used to identify the top ten causes of cancer death in 2013. All journal articles and clinical trials published in 2013 by Canadian or U.S. authors for those cancers were identified. Total research funding in Canada by cancer site was obtained from the Canadian Cancer Research Alliance. Descriptive statistics and Pearson correlation coefficients were used to describe the relationship between research output, cancer mortality, and research funding. RESULTS: We identified 19,361 publications and 2661 clinical trials. The proportion of publications and clinical trials was substantially lower than the proportion of deaths for lung (41% deaths, 15% publications, 16% clinical trials), colorectal (14%, 7%, 6%), pancreatic (10%, 7%, 5%), and gastroesophageal (7%, 5%, 3%) cancers. Conversely, research output was substantially greater than the proportion of deaths for breast cancer (10% deaths, 29% publications, 30% clinical trials) and prostate cancer (8%, 15%, 17%). We observed a stronger correlation between research output and funding (publications r = 0.894, p < 0.001; clinical trials r = 0.923, p < 0.001) than between research output and cancer mortality (r = 0.363, p = 0.303; r = 0.340, p = 0.337). CONCLUSIONS: Research output is not well correlated with the public health burden of individual cancers, but is correlated with the relative level of research funding.
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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 | 0.064 | 0.375 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.056 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".