Arm-trunk coordination as a measure of vestibulospinal efficiency
Bibliographic record
Abstract
When arm and trunk segments are involved in reaching for objects within arm's reach, vestibulospinal pathways compensate for trunk motion influence on arm movement. This compensatory arm-trunk synergy is characterised by a gain coefficient of 0 to 1. Vestibular patients have less efficient arm-trunk synergies and lower gains. To assess the clinical usefulness of the gain measure, we used a portable ultrasound-based device to characterize arm-trunk coordination deficits in vestibular patients. Arm-trunk coordination without vision was measured in a Stationary Hand Task where hand position was maintained during trunk movement, and a Reaching Task with and without trunk motion. Twenty unilateral vestibular patients and 16 controls participated. For the Stationary Hand task, patient gains ranged from g=0.94 (good compensation) to 0.31 (poor compensation) and, on average, were lower than in controls (patients: 0.67 ± 0.19; controls: 0.85 ± 0.07; p< 0.01). Gains were significantly correlated with clinical tests (Sensory Organization; r=0.62, p< 0.01, Foam Romberg Eyes Closed; r=0.65, p< 0.01). For the Reaching Task, blocking trunk movement during reaching modified hand position significantly more in patients (8.2 ± 4.3 cm) compared to controls (4.5 ± 1.7 cm, p< 0.02) and the amount of hand position deviation was correlated with the degree of vestibular loss in a sub-group (n=14) of patients. Measurement of the Stationary Task arm-trunk gain and hand deviations during the Reaching Task can help characterize sensorimotor problems in vestibular-deficient patients and track recovery following therapeutic interventions. The ultrasound-based portable device is suitable for measuring vestibulospinal deficits in arm-trunk coordination in a clinical setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".