Hydrocephalus research funding from the National Institutes of Health: a 10-year perspective
Bibliographic record
Abstract
OBJECT: Funding of hydrocephalus research is important to the advancement of the field. The goal of this paper is to describe the funding of hydrocephalus research from the National Institutes of Health (NIH) over a recent 10-year period. METHODS: The NIH online database RePORT (Research Portfolio Online Reporting Tools) was searched using the key word "hydrocephalus." Studies were sorted by relevance to hydrocephalus. The authors analyzed funding by institute, grant type, and scientific approach over time. RESULTS: Over $54 million was awarded to 59 grantees for 66 unique hydrocephalus proposals from 48 institutions from 2002 to 2011. The largest sources of funding were the National Institute of Neurological Disease and Stroke and the National Institute of Child Health and Human Development. Of the total, $22 million went to clinical trials, $15 million to basic science, and $10 million to joint ventures with small business (Small Business Innovation Research or Small Business Technology Transfer). Annual funding varied from $2.3 to $8.1 million and steadily increased in the second half of the observation period. The number of new grants also went from 15 in the first 5 years to 27 in the second 5 years. A large portion of the funding has been for clinical trials. Funding for shunt-device development grew substantially. Support for training of hydrocephalus investigators has been low. CONCLUSIONS: Hydrocephalus research funding is low compared with that for other conditions of similar health care burden. In addition to NIH applications, researchers should pursue other funding sources. Small business collaborations appear to present an opportunity for appropriate projects.
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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 | 0.044 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| 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".