Observing the Diversity of Alleviating Manoeuvres in Cervical Dystonia
Bibliographic record
Abstract
The alleviating manoeuvres (AMs), classically referred to as “sensory tricks” are voluntary manoeuvres that temporarily improve dystonic postures. Although self-induced application of sensory stimuli is the most common AM, clinical experience suggests that the phenomenon is more diverse, possibly reflecting the complexity of the pathophysiological mechanisms provoking dystonia. We specifically explored five different categories of AMs in patients with cervical dystonia (CD): 1) pure sensory; sensorimotor manoeuvres in which sensory input is associated with a motor output component incorporating 2) active non-oppositional, 3) active oppositional or 4) passive motion; and 5) complex motor manoeuvres. Using an ad hoc structured clinical interview, we collected data on the frequency and efficacy of each subgroup and the possible correlation with some clinical features of CD. One-hundred patients were included in this study. Seventy-five percent of patients reported at least one AM. Half of those reporting AMs acknowledged the use of different phenomenological categories of AMs. Different categories of AMs showed noteworthy differences in prevalence of use amongst CD patients, and in the relationship of frequency of use and efficacy to patient demographic and clinical characteristics. Our observational study supports the existence of different AMs that are phenomenologically different and could be related to different degrees of sensorimotor integration dysfunction. Given that AMs are probably the most efficacious, non-invasive strategy to ameliorate CD and other dystonias, accurate phenotyping and physiological exploration of their diversity may produce relevant insight for new therapeutic strategies or appraisal of existing ones.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".