Challenges and opportunities in testing sensorimotor processing with tendon vibration and transcranial magnetic stimulation in subacromial impingement syndrome: A case series
Bibliographic record
Abstract
BACKGROUND: Non-invasive neurostimulation like muscle tendon vibration (VIB) and transcranial magnetic stimulation (TMS) can provide valuable insights on mechanisms underlying sensorimotor dysfunctions. However, their feasibility in the context of painful musculoskeletal disorders like shoulder impingement syndrome (SIS) remain uncertain. METHODS: The present work used a case series design including 15 participants with SIS, as well as a secondary group-based analysis comparing participants with SIS to 15 healthy counterparts. Proprioceptive processing was tested by VIB-induced kinesthetic illusions of shoulder abduction, and TMS tested corticospinal excitability of the upper trapezius. Detailed individual data were collected, including any technical challenges and feasibility issues encountered. RESULTS: VIB was in general well-tolerated and elicited a perceptible kinesthetic illusion in 13 participants with SIS and 14 controls. TMS presented with several challenges related to discomfort, fear-related behaviors, technical problems and high motor thresholds, especially in participants with SIS. It was only possible to collect all TMS measures in 5 participants with SIS (for both the painful and non/less-painful sides), in 7 controls on their dominant side and 10 controls on the non-dominant side. The only significant group-based analysis was a lower illusion speed/amplitude on the painful versus non-painful side in persons with SIS (p = 0.035). CONCLUSION: Our study provides preliminary data on challenges encountered with TMS and VIB of trunk/proximal muscle in persons with SIS and healthy counterparts. It might help future studies to better address those challenges beforehand and improve the overall feasibility and impact of neurostimulation tools in musculoskeletal disorders.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".