J015 The effect of antidopaminergic medications on Huntington disease (HD) progression
Bibliographic record
Abstract
Background Antidopaminergics (ADMs; VMAT2 inhibitors and neuroleptics [off-label]), are used for the symptomatic treatment of HD and are associated with faster rates of decline. To date, no prospective, double-blind studies have assessed the long-term effects of ADMs on HD progression, but observational data are available. Aims To assess the effect of ADMs on rates of progression in early HD (TMS ≥ 20, DCL = 4, TFC ≥ 7, IS ≤ 90) using the ENROLL-HD database. Methods Causal analysis was performed using a new-user design to account for use history. Participants were not on medication at the first visit, but the exposure group (N = 380) was on medication for the 2-year follow-up, whereas the unexposed group (N = 792) was always off. Target maximum likelihood estimation was used to estimate the mean difference of the groups at 2 years adjusting for 28 confounders. Outcomes were the main UHDRS variables, including the composite (cUHDRS), and motor subscales. 99% CIs were used for inference. Results Results show participants on ADMs had a significantly smaller mean compared to those off for TFC (99% CI = [-1.04, -.28]), SDMT [-2.90, -1.07], SWR [-4.80, -1.04], and cUHDRS [-.96,-.32]. The on-ADMs group had a significantly larger mean for the bradykinesia scale [.02, 1.72]. Other outcomes had CIs that covered 0 figure 1. Conclusion ADM use was associated with faster progression of functioning and cognitive performance. These observations have important implications for the conduct and interpretation of investigational studies of disease modifying agents in HD and for medical practice.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".