Cervical dystonia: a neural integrator disorder
Bibliographic record
Abstract
Ocular motor neural integrators ensure that eyes are held steady in straight-ahead and eccentric positions of gaze. Abnormal function of the ocular motor neural integrator leads to centripetal drifts of the eyes with consequent gaze-evoked nystagmus. In 2002 a neural integrator, analogous to that in the ocular motor system, was proposed for the control of head movements. Recently, a counterpart of gaze-evoked eye nystagmus was identified for head movements; in which the head could not be held steady in eccentric positions on the trunk. These findings lead to a novel pathophysiological explanation in cervical dystonia, which proposed that the abnormalities of head movements stem from a malfunctioning head neural integrator, either intrinsically or as a result of impaired cerebellar, basal ganglia, or peripheral feedback. Here we briefly recapitulate the history of the neural integrator for eye movements, then further develop the idea of a neural integrator for head movements, and finally discuss its putative role in cervical dystonia. We hypothesize that changing the activity in an impaired head neural integrator, by modulating feedback, could treat dystonia. Dysfunction of the oculomotor neural integrator leads to gaze-induced nystagmus. Shaikh et al. briefly recapitulate the basic principles behind neural integration, before developing the idea of an analogous neural integrator for head movements and the putative role of such an integrator in cervical dystonia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".