F010 Shield-HD: a longitudinal natural history study with implications for interventional trials
Bibliographic record
Abstract
Background Shield-HD is a longitudinal natural history study well-positioned to answer questions about how biomarkers, clinical assessments, and volumetric MRI data interrelate over the course of disease progression. Participants were recruited and assigned to three cohorts based on CAP score (<290, 290–400, >400). Ultimately, N = 68 subjects passed screening and were followed for up to 120 weeks. Aims Our goals are threefold: 1) To explore the role of the Huntington’s Disease Integrated Staging System (HD-ISS) in Shield and its adoption in future studies. 2) To estimate departures from baseline for common endpoints and to compare these results to those from previous studies. 3) To estimate correlations between outcomes, fully leveraging Shield’s high-resolution multimodal data. Methods Analyses are based on regression techniques for correlated error terms (such as mixed models). Subject-specific effects are extracted from these models in order to compute correlations between multiple longitudinal variables. Results are augmented with data from Enroll PDS6 and Track-HD/TrackOn-HD. Results We mention several results pertaining to the second goal above. Putamen and Caudate volumes declined at estimated rates of 1.9% and 6.5% per year respectively. Concentrations of plasma NfL increased slightly (1.0 ng/L per year) while no analogous changes were found in the CSF. Clinical composite scores showed either no significant changes (HD-CAB) or slight decline (cUHDRS; .3 points per year). These statistically detectable changes from baseline were generally smaller than expected. Conclusions Shield-HD is particularly valuable in guiding future interventional trials. We emphasize this perspective throughout, especially with respect to recruitment and the selection of endpoints.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".