Short-latency afferent inhibition and its relationship to covert sensory and motor hand impairment in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To investigate sensorimotor integration by quantifying short-latency afferent inhibition (SAI) in people with MS who experience manual dexterity problems compared to controls. METHODS: 22 people with MS with self-reported manual dexterity problems and 10 sex and age-matched controls were assessed using various upper extremity clinical tests. SAI was assessed by a transcranial magnetic stimulation pulse over the primary motor cortex preceded by peripheral nerve stimulation to the median nerve at 6 interstimulus intervals 2 - 8 ms longer than individualized N20 latencies. RESULTS: Although within normal limits, persons with MS exhibited significantly slower Nine Hole Peg Test performance and pinch strength in the dominant hand. They also exhibited greater sensory impairment (monofilament test) in the dominant hand. Persons with MS showed significantly greater disinhibition of SAI in the dominant hand compared to controls, which was significantly correlated with weaker pinch strength. CONCLUSION: Reduced SAI in people with MS, particularly in the dominant hand, signifies disruptions in cortical cholinergic inhibitory activity and is associated with lower pinch strength. SIGNIFICANCE: Evaluating changes in SAI may offer insight into the disrupted cortical cholinergic inhibitory activity that contributes to sensorimotor disintegration, potentially advancing disease management in persons with MS.
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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) | 0.001 | 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".