Validation Study of a German Cognitive Battery for Huntington’s Disease: Relationship Between Cognitive Performance, Functional Decline, and Disease Burden
Bibliographic record
Abstract
OBJECTIVE: Cognitive decline is a key characteristic of Huntington's disease (HD). This study aimed to investigate the diagnostic accuracy of a cognitive battery with six tests used by most HD research centers to assess cognitive impairment in HD. METHOD: In total, 106 HD patients in different disease stages with more (HD-CD, N = 30) and less cognitive impairments (HD-NC, N = 70) and 100 healthy controls (NC) were matched by age, sex, and education and were examined using a standardized protocol including cognitive, motor, and functional assessments. RESULTS: One-way between-groups analysis of variance showed that controls performed significantly better than HD patients and that HD-NC significantly outperformed HD-CD patients in all cognitive tests (NC > HD-NC > HD-CD), with all Games-Howell post-hoc tests p < .001. Analyses using area under the receiver-operating characteristic curve (AUC) disclosed the diagnostic accuracy of all tests included in the battery to discriminate between NC and HD patients with AUC ranging from 0.809 to 0.862 (all p < .001) and between HD-CD and HD-NC patients with AUC ranging from 0.833 to 0.899 (all p < .001). In both analysis, Stroop Color Naming Test showed the highest discriminative potential. Additional analyses showed that cognitive deficits in all domains progressed with disease duration. Moreover, cognitive performance correlated with the severity of motor and functional impairment (all p < .001) and with the Disease Burden Score regardless of disease duration and age. CONCLUSION: Our results indicate that the cognitive battery is a suitable tool for assessing cognitive impairment in HD.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".