Funding agencies and disease organizations: Resources and recommendations to facilitate ALS clinical research
Bibliographic record
Abstract
Ten groups presented their perspectives on facilitating clinical research in ALS including four federal agencies, four disease organizations, one foundation and one advocacy group. The federal agencies (National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, Office of Rare Diseases Research, Department of Defense) encourage fostering a team approach between pre-clinical and clinical research investigators, coordinating with patient groups in the early phases of clinical studies, enhancing private and public partnerships, and investigating the interplay between genetic susceptibility and environmental exposure. The disease organizations (Muscular Dystrophy Association, ALS Association, ALS Society of Canada, and the Motor Neurone Disease Association UK) support fellowship training programs to develop ALS clinician scientists, and encourage work on the epidemiology of ALS, on genetic and epigenetic mechanisms that are relevant to ALS pathogenesis, on developing ALS registries and biobanks, and building bridges of collaboration among study groups. The Foundation supports innovative projects, including stem-cell research, and Patient Advocacy is committed to supporting excellence in ALS research and patient care, and believes strongly in enhancing communication between patients and members of the research community.
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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 | 0.121 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".