Impact of a grant program to spur advances in sickle cell disease research
Bibliographic record
Abstract
More than 20 years ago, clinical trials and federal grant support for sickle cell disease (SCD) research were not on par with support for other genetic diseases. Faced with the opportunity to spur research and advance treatments for SCD, and at the recommendation of advisors, the Doris Duke Charitable Foundation (DDCF) offered an SCD research funding opportunity starting in 2009 through its Innovations in Clinical Research Awards (ICRA) program. Twenty-eight new grants of $450 000 for direct costs over 3 years and 7 renewals were awarded, for a total investment of $17 million. Only about half the research teams garnered follow-on funding directly related to their ICRA projects, but the financial return on the research investment was substantial (∼4 times the original $17 million or 300%). All but 1 of the ICRA investigative teams published original research reports that acknowledged DDCF as a source of funding; the median number of publications per team was 3. Major innovations in the diagnosis and treatment of SCD included but were not limited to a demonstration that genetic modification of BCL11A enhancer is a potentially important treatment modality, establishment that plerixafor mobilization is safe and effective for those with SCD, development and validation of a new diagnostic called SCD BioChip, and evidence that hydroxyurea treatment is safe and efficacious in African children. These outcomes show that relatively small research grants can have a substantial return on investment and result in significant advances for a disease such as SCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".