Comparison of cognitive and UHDRS measures in monitoring disease progression in Huntington’s disease: a 12-month longitudinal study
Bibliographic record
Abstract
Progressive cognitive decline is a feature of Huntington's disease (HD), an inherited neurodegenerative movement disorder. Comprehensive neuropsychological testing is the 'gold standard' to establish cognitive status but is often impractical in time-constrained clinics. The study evaluated the utility of brief cognitive tests (MMSE and MoCA), UHDRS measures and a comprehensive neuropsychological tests battery in monitoring short-term disease progression in HD. Twenty-two manifest HD patients and 22 matched controls were assessed at baseline and 12-month. A linear mixed-effect model showed that although the HD group had minimal change in overall global cognition after 12 months, they did show a significant decline relative to the control group. The controls exhibited a practice effect in most of the cognitive domain scores over time. Cognitive decline at 12-month in HD was found in the executive function domain but the effect of this on global cognitive score was masked by the improvement in their language domain score. The varying practice effects by cognitive domain with repeated testing indicates the importance of comparing HD patients to control group in research trials and that cognitive progression over 12 months in HD should not be judged by changes in global cognitive score. The three brief cognitive tests effectively described cognition of HD patients on cross-sectional analysis. The UHDRS cognitive component, which focuses on testing executive function and had low variance over time, is a more reliable brief substitute for comprehensive neuropsychological testing than MMSE and MoCA in monitoring cognitive changes in HD patients after 12 months.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".