Assessing proprioceptive acuity in people with multiple sclerosis
Bibliographic record
Abstract
Background: Proprioceptive acuity and impairments in proprioceptively guided reaches have not been comprehensively examined in people with multiple sclerosis (MS). Objective: To examine proprioceptive acuity in people with MS who self-report and who do not self-report upper limb (UL) impairment, and to determine how people with MS reach proprioceptive targets. Methods: = 12). Proprioception was assessed using ipsilateral and contralateral robotic proprioceptive matching tasks. Results: Participants in the MS-R group demonstrated worse proprioceptive acuity compared to the MS-NR group on the ipsilateral and contralateral robotic matching tasks. Analyses of reaches to proprioceptive targets further revealed that participants in the MS-R group exhibited deficits in movement planning, as demonstrated by greater errors at peak velocity in the contralateral matching task in comparison to the MS-NR group. Conclusion: Our findings suggest that people with MS who self-report UL impairment demonstrate worse proprioceptive acuity, as well as poorer movement planning in comparison to people with MS who do not report UL impairment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".