Antidopaminergic Medications and Clinical Changes in Measures of Huntington's Disease: A Causal Analysis
Bibliographic record
Abstract
BACKGROUND: Antidopaminergic medications (ADM) are often used for symptom management of Huntington's disease (HD). Evidence from past research suggests that ADMs are associated with worse clinical outcomes in HD, but their impact on various domains remains underexplored. OBJECTIVE: We used causal inference analysis to understand the impact of ADM use on measures of clinical progression in HD across multiple domains over 2 years. METHODS: We used the Enroll-HD database with a new-user design, which compared a cohort that initiated ADM use after the first visit with an unexposed cohort that remained off ADMs. To control for 27 covariates, we used a doubly robust targeted maximum likelihood estimation and conducted two analyses. First, we analyzed ADM treatment 2 years post-baseline and separately for 12 outcome measures. Second, we examined the association of ADM dose with measures of clinical outcomes. RESULTS: The ADM-exposed group exhibited faster change in measures of clinical outcome compared with the off-ADM group, which was statistically reliable in cognitive and functional outcome measures, and the composite Unified Huntington's Disease Rating Scale (cUHDRS). Motor domain analyses showed faster change in bradykinesia in the ADM-exposed group versus off-ADM but no difference in chorea or total motor score (TMS). Higher ADM doses also showed greater differences compared to the off-ADM group. CONCLUSIONS: ADM use was associated with more rapid change in clinical measures, particularly in cognitive and functional domains. However, assumptions required to establish causation between ADM use and disease progression may not have been fully met, and further research is warranted. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".