Antidopaminergic Medications Are Associated with Faster Decline in Measures of Clinical Outcome in HD: Insights from PROOF-HD
Bibliographic record
Abstract
Abstract Background Antidopaminergic medications (ADMs), including vesicular monoamine transporter-2 (VMAT2) inhibitors and antipsychotics, are frequently-used to manage Huntington disease (HD) symptoms. Prior studies suggest that ADMs may be associated with worsening on measures of outcome in HD clinical trials. The PROOF-HD placebo arm ( NCT04556656 ) provided a controlled, double-blind setting to evaluate ADM impacts on measures of HD progression. Objective Assess the association between ADM exposure and change in clinical outcomes in the placebo arm of PROOF-HD. Methods Placebo-arm participants (n=247) were categorized as on- vs off-ADMs. Overall main analyses were corroborated by propensity-score weighting (PSW)-adjusted analyses. Unadjusted analyses examined exposure by ADM class and dose. Outcomes included Total Functional Capacity (TFC), composite Unified Huntington’s Disease Rating Scale (cUHDRS), Stroop Word Reading (SWR), Symbol Digit Modalities Test (SDMT), and Total Motor Score (TMS). Results Group differences (Δ) favored off-ADMs in cUHDRS (Weeks 39–78) and TFC (Weeks 26–78); at Week 52, cUHDRS had Δ=0.66 (95% CI 0.31–1.01; p=0.0002) and TFC with Δ=0.85 (95% CI 0.47–1.22; p<0.0001) as compared with on-ADMs. Other outcomes were significant or directionally-favored off-ADM participants beyond Week 39. All TMS-subdomain scores, except for chorea, directionally-favored off-ADMs at all visits. Antipsychotic-only and higher-dose ADMs were associated with worse cUHDRS and TFC vs off-ADMs. Conclusions In this post hoc study, ADM use was associated with greater worsening of measures of global, functional, cognitive, and motor outcomes, versus off-ADMs. Accounting for ADM exposure and dose is essential for the interpretation of results from HD trials.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".