Impact of subclinical neck pain on eye and hand movements in goal-directed upper limb aiming movements
Bibliographic record
Abstract
Individuals with untreated, mild-to-moderate recurrent neck pain or stiffness (subclinical neck pain (SCNP)) have been shown to have impairments in upper limb proprioception, and altered cerebellar processing. It is probable that aiming trajectories will be impacted since individuals with SCNP cannot rely on accurate proprioceptive feedback or feedforward processing (body schema) for movement planning and execution, due to altered afferent input from the neck. SCNP participants may thus rely more on visual feedback, to accommodate for impaired cerebellar processing. This quasi-experimental study sought to determine whether upper limb kinematics and oculomotor processes were impacted in those with SCNP. 25 SCNP and 25 control participants who were right-hand dominant performed bidirectional aiming movements using two different weighted styli (light or heavy) while wearing an eye-tracking device. Those with SCNP had a greater time to and time after peak velocity, which corresponded with a longer upper limb movement and reaction time, seen as greater constant error, less undershoot in the upwards direction and greater undershoot in the downwards direction compared to controls. SCNP participants also showed a trend towards a quicker ocular reaction and movement time compared to controls, while the movement distance was fairly similar between groups. This study indicates that SCNP alters aiming performances, with greater reliance on visual feedback, likely due to altered proprioceptive input leading to altered cerebellar processing.
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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 | 0.000 | 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".