Dynamic Upper Limb Proprioception in Multidirectional Shoulder Instability
Bibliographic record
Abstract
The ability of subjects with multidirectional shoulder instability to use proprioception to complete a series of upper limb repositioning tasks was investigated. Twelve subjects with multidirectional instability and 12 control subjects were blindfolded and instructed to use proprioception to reproduce a self-selected target position as accurately as possible. Subjects completed 10 repetitions for each limb using three distinct upper limb movements: overhead reaching, scapular plane pointing, and humeral external rotation with abduction. A three-dimensional video motion analysis system tracked limb position and determined spatial hand position error. Subjects with multidirectional shoulder instability showed significantly greater hand position error than control subjects. No hand position error differences were found between the symptomatic and the asymptomatic limbs of subjects in the instability group. Inter-repetition error for subjects in both groups improved significantly during the first three movement cycles. These results suggest that after movement initiation, dynamic proprioception was a factor in improving hand position accuracy in both groups, but to a lesser degree in subjects with multidirectional instability. Consequently, subjects with multidirectional instability may have a reduced capacity to use proprioception to refine and control the motor output of the upper limb.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".