Enroll-HD: A Global Clinical Research Platform for Huntington’s Disease (S25.005)
Bibliographic record
Abstract
Objective: Enroll-HD is a platform for clinical research that includes a global observational registry study of Huntington’s disease (HD). Enroll-HD offers easy access to phenotypic data and renewable biosamples for researchers. Background: HD is a neurodegenerative disease caused by an unstable CAG expansion on chromosome 4. Current prevalence estimates for manifest HD are 7-10 per 100,000. Registries of rare diseases, like HD, offer the opportunity for collecting real-world data and biologic samples that provide phenotypic and genotypic data on well-characterized cohorts. Many of these registries are frequently limited to regional efforts with limited access to data and samples. To be successful in aiding clinical trial recruitment, understanding complex disease mechanisms and improving patient care these efforts have to be global and offer easier access to data and biological samples and provide a research infrastructure that supports collaboration. Methods: Enroll-HD is a global clinical research platform with goals to facilitate clinical trials, understand HD and improve clinical care. Study participants undergo an annual assessment on a core data set covering motor, cognitive, behavioral symptoms with optional assessments exploring work productivity measures, quality of life and physical functioning. Enroll-HD enables the collection, banking and distribution of biologic samples (lymphocytes, lymphoblastoid cell lines and DNA). Phenotypic data are available at regular intervals through a straightforward downloading process. Results: Enroll-HD currently has 7,993 participants at 125 sites globally a dataset and bio-specimens from 3,400 participants are available to researchers. The process for data and biological sample access and the platform infrastructure is presented. Conclusions: Enroll-HD represents the most geographically diverse collection of data and samples in HD. Enroll-HD is a research platform that brings together an unprecedented set of tools for HD clinical research and is an important resource for HD researchers and those interested in neurodegenerative diseases.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.029 |
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".