Apathy predicts rate of cognitive decline over 24 months in premanifest Huntington's disease
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a core feature of Huntington's disease (HD), however, the onset and rate of cognitive decline is highly variable. Apathy is the most common neuropsychiatric symptom of HD, and is associated with cognitive impairment. The aim of this study was to investigate apathy as a predictor of subsequent cognitive decline over 2 years in premanifest and early HD, using a prospective, longitudinal design. METHODS: A total of 118 premanifest HD gene carriers, 111 early HD and 118 healthy control participants from the multi-centre TRACK-HD study were included. Apathy symptoms were assessed at baseline using the apathy severity rating from the Short Problem Behaviours Assessment. A composite of 12 outcome measures from nine cognitive tasks was used to assess cognitive function at baseline and after 24 months. RESULTS: In the premanifest group, after controlling for age, depression and motor signs, more apathy symptoms predicted faster cognitive decline over 2 years. In contrast, in the early HD group, more motor signs, but not apathy, predicted faster subsequent cognitive decline. In the control group, only older age predicted cognitive decline. CONCLUSIONS: Our findings indicate that in premanifest HD, apathy is a harbinger for cognitive decline. In contrast, after motor onset, in early diagnosed HD, motor symptom severity more strongly predicts the rate of cognitive decline.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".