Visual control improves the accuracy of hand positioning in Huntington’s disease
Bibliographic record
Abstract
Background: The study aimed at demonstrating dependence of visual feedback during hand and finger positioning task performance among Huntington’s disease patients in comparison to patients with Parkinson’s disease and cervical dystonia. Material and methods: Eighty-nine patients participated in the study (23 with Huntington’s disease, 25 with Parkinson’s disease with dyskinesias, 21 with Parkinson’s disease without dyskinesias, and 20 with cervical dystonia), scoring ≥20 points on Mini-Mental State Examination in order to assure comprehension of task instructions. Neurological examination comprised of the motor section from the Unified Huntington’s Disease Rating Scale for Huntington’s disease, the Unified Parkinson’s Disease Rating Scale Part II–IV for Parkinson’s disease and the Toronto Western Spasmodic Torticollis Rating Scale for cervical dystonia. In order to compare hand position accuracy under visually controlled and blindfolded conditions, the patient imitated each of the 10 examiner’s hand postures twice, once under the visual control condition and once with no visual feedback provided. Results: Huntington’s disease patients imitated examiner’s hand positions less accurately under blindfolded condition in comparison to Parkinson’s disease without dyskinesias and cervical dystonia participants. Under visually controlled condition there were no significant inter-group differences. Conclusions: Huntington’s disease patients exhibit higher dependence on visual feedback while performing motor tasks than Parkinson’s disease and cervical dystonia patients. Possible improvement of movement precision in Huntington’s disease with the use of visual cues could be potentially useful in the patients’ rehabilitation.
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 | 0.001 |
| 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) | 0.001 | 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".