Single‐Interval and Rhythmic Temporal Prediction in Cervical Dystonia
Bibliographic record
Abstract
BACKGROUND: Cerebellar dysfunction disrupts memory-based temporal predictions (TPs), whereas basal ganglia dysfunction affects rhythm-based TPs. Investigating TPs in cervical dystonia (CD) may help to delineate the contributions of subcortical circuits to CD pathophysiology. OBJECTIVE: The aim of this study was to explore TP ability in patients with CD compared with healthy control subjects (HCs) and to examine the relationship between TPs and clinical features of CD. METHODS: Twenty patients with CD and 20 HCs completed a TP task. Reaction times (RTs) were measured during TPs under three conditions: rhythmic and single-interval (predictable target onset) and random (unpredictable target onset). RT benefit scores were calculated by subtracting RTs in the random condition from those in predictive conditions. RESULTS: Our exploratory analysis showed that patients with CD had lower benefit scores than HCs in the single-interval task. In CD, benefit scores in the single-interval task were negatively correlated with Toronto Western Spasmodic Torticollis Rating Scales severity. CONCLUSIONS: Patients with CD exhibited selective impairments in interval-based predictions, suggesting cerebellar involvement in dystonia's pathophysiology. © 2025 International Parkinson and Movement Disorder Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".