Sensory tricks in primary cervical dystonia depend on visuotactile temporal discrimination
Bibliographic record
Abstract
A characteristic feature of primary cervical dystonia is the presence of "sensory tricks" as well as the impairment of temporal and spatial sensory discrimination on formal testing. The aim of the present study was to test whether the amount of improvement of abnormal head deviation due to a sensory trick is associated with different performance of temporal sensory discrimination in patients with cervical dystonia. We recruited 32 patients with cervical dystonia. Dystonia severity was assessed using the Toronto Western Spasmodic Torticollis Rating Scale. Patients were rated according to clinical improvement to a sensory trick and assigned to 1 of the following groups: (1) no improvement (n = 6), (2) partial improvement (n = 17), (3) complete improvement (n = 9). Temporal discrimination thresholds were assessed for visual, tactile, and visuotactile modalities. Disease duration was shorter (P = .026) and dystonia severity lower (P = .033) in the group with complete improvement to sensory tricks compared with the group with partial improvement to sensory tricks. A significant effect for group and modality and a significant interaction between group × modality were found, with lower visuotactile discrimination thresholds in the group with complete improvement to sensory tricks compared with the other groups. In primary cervical dystonia, a complete resolution of dystonia during a sensory trick is associated with better visuotactile discrimination and shorter disease duration compared with patients with less effective sensory tricks, which may reflect progressive loss of adaptive mechanisms to basal ganglia dysfunction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".