Primary and submovement control of aiming in C6 tetraplegics following posterior deltoid transfer
Bibliographic record
Abstract
BACKGROUND: Upper limb motor control in fast, goal-directed aiming is altered in tetraplegics following posterior-deltoid musculotendinous transfer. Specifically, movements have similar end-point accuracy but longer duration and lower peak velocity than those of age-matched, neurotypical controls. Here, we examine in detail the interplay between primary movement and submovement phases in five C6 tetraplegic and five control participants. METHODS: Aiming movements were performed in two directions (20 cm away or toward), with or without vision. Trials that contained a submovement phase (i.e., discontinuity in velocity, acceleration or jerk) were identified. Discrete kinematic variables were then extracted on the primary and submovements phases. RESULTS: The presence of submovements did not differ between the tetraplegic (68%) and control (57%) groups, and almost all submovements resulted from acceleration and jerk discontinuities. Tetraplegics tended to make a smaller amplitude primary movement, which had lower peak velocity and greater spatial variability at peak velocity. This was followed by a larger amplitude and longer duration secondary submovement. Peak velocity of primary movement was not related to submovement incidence. Together, the primary and submovement phases of both groups were equally effective in reducing end-point error. CONCLUSIONS: C6 tetraplegic participants exhibit some subtle differences in measures of motor behaviour compared to control participants, but importantly feedforward and feedback processes work effectively in combination to achieve accurate goal-directed aiming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".