A REVIEW ON CANCER RESEARCH FUNDING
Bibliographic record
Abstract
Cancer is one of the major causes of illness worldwide and the second most important cause of death. The burden of this disease goes beyond the individuals and their families to health caregivers and society. Many actors contribute to the management of cancer in a specific society, and one of the most important issues related to this is cancer research funding, which provides the opportunity for advances in cancer Biology, Etiology, Prevention, Early Detection, Treatment, Cancer Control and Scientific Model Systems. In this paper we presented a literature review on cancer research funding. We analysed the amount of money invested in cancer research in different regions, the type of funding organizations, the distribution of cancer research funding by percentage of Gross Domestic Product, by Common Scientific Outline categories and by cancer site. The results show that USA and Europe had the major spending in cancer research, followed by Canada, Australia, and Asia (especially Japan and China). Among European countries United Kingdom has allocated the largest funds for cancer research. The major funders for oncological research were the governmental organizations in USA, Canada, Australia, and both, governmental and charities, in Europe. In recent years China made impressive progress in cancer research funding, but is still far behind the Western countries. For all regions the majority of funding for cancer research was directed to Biology and Treatment, and less oriented to Prevention and Cancer control. However, in order to direct the funding for cancer research accordingly to burden of disease in different populations and societies, further research is needed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".