Bibliographic record
Abstract
Entrapment of the ulnar nerve at the elbow is the second most common focal peripheral neuropathy. Recent advances have facilitated the electrodiagnosis of this common nerve entrapment. The goals of electrodiagnosis are to localize ulnar nerve dysfunction, confirm that the disturbance is confined to the ulnar nerve, and assess the severity of ulnar nerve dysfunction. The goal of this review is to highlight the important advances in anatomy, neurophysiology and methodology that impact upon the electrodiagnosis of entrapment of the ulnar nerve at the elbow, illustrate the limits of electrodiagnosis, and discuss methodological issues that may be the subject of further study. Careful attention to elbow position, temperature, and conservative estimates of conduction block should be part of common practice. Awareness of anatomical variations in structural anatomy, anomalous innervation and fascicular arrangement of ulnar nerve fibers are required to interpret electrodiagnostic studies accurately. The most reliable finding is slowing of the ulnar across-elbow motor nerve conduction velocity to less than 50 m/sec while recording from the abductor digiti minimi muscle, and should be carefully interpreted in the presence of a polyneuropathy or other neurogenic process. Alternative techniques such as relative ulnar slowing in different ulnar nerve segments, use of alternative muscles, sensory and mixed nerve techniques provide complementary information, and like all nerve conduction studies are highly operator-dependent and should be used on a case by case basis. Recent studies have focused the electromyographer's attention on the use of shorter across-elbow segments (2-5 cm). This may offer a reasonable trade-off between sensitivity and measurement error and may result in improved electrodiagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".