Effect of Demyelinating Ulnar Nerve Injury on Strength and Fatigue
Bibliographic record
Abstract
OBJECTIVES: Conduction block (CB) from focal neuropathy is often associated with weakness and fatigue in affected muscles. Ulnar neuropathy at the elbow (UNE) provides an excellent model to examine the relationships between electrophysiologically defined CB and quantitative measurement of weakness and fatigue. METHODS: Eight healthy control subjects (47 ± 14 years) and nine patients (53 ± 3 years) with clinical and electrophysiological features of UNE with CB were studied. All underwent bilateral, ulnar motor nerve conduction studies recording from the first dorsal interosseous muscle as well as quantitative measurement of strength and fatigue of the first dorsal interosseous with a custom dynamometer. RESULTS: Strength and fatigue were similar in the dominant and nondominant hands of control subjects and unaffected limb in patients. Varying degrees of conduction block (14-62%, mean 36%) and conduction slowing (31 m/s ± 7) were observed in those with UNE. CB was associated with significant reductions in strength (42%) and fatigue (23%) on a timed fatigue task. The reductions in strength (r = 0.74) and fatigue (r = 0.60) were strongly correlated with the degree of CB. CONCLUSIONS: CB in UNE defined by electrophysiological criteria was strongly correlated with weakness and fatigue in the first dorsal interosseous. Fatigue may be simply related to the reduction in strength, but activity or frequency dependent CB may also contribute.
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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 | 0.002 |
| 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 | 0.000 |
| 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".