Analysis of tauopathy research funding between 2006 and 2016 reveals critical gaps in research priorities
Bibliographic record
Abstract
Neurodegenerative diseases encompass a range of diagnoses, such as Alzheimer's disease and Parkinson's disease. Despite decades of advancements in understanding the neurobiology of individual diseases, this class has few disease-modifying therapeutics and a paucity of biomarkers for diagnosis or progression. However, tau protein aggregation has emerged as a potential unifying factor across several neurodegenerative diseases, which has prompted a rapid growth in tau-related funding. In spite of this growth, research funding in this area is not in line with the immense magnitude of disease burden, and drug discovery and clinical research remain underfunded. Coordinated, collaborative efforts are key to making an impact, which can and should be led by the major funding bodies within the tau space. Here we describe the development and analysis of a tau-focused neurodegeneration funding database, which captures data from 2040 grants from 2006 to 2016. This database was developed as a public resource to allow funders, researchers, and policy makers to better understand tau funding patterns and to identify key funders and potential collaborations. This database can be used in conjunction with other neurodegenerative disease databases, such as the International Alzheimer's Disease Research Portfolio to gain specific insight into tau-research funding. Over the study period, overall tau funding rose dramatically; however, changes in capital distribution also changed. Specifically, the field experienced a strong bias toward funding tau in the context of Alzheimer's disease, while at the same time generally decreasing the overall proportion of funding for basic research, treatment development, and evaluation. As funding organizations look forward, this resource can both inform future funding strategies and priority areas and identify potential collaborative efforts with complementary funding organizations.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".