Multimodality imaging review of ulnar nerve pathologies
Bibliographic record
Abstract
The ulnar nerve is the second most commonly entrapped nerve after the median nerve. Although clinical evaluation and electrodiagnostic studies remain widely used for the evaluation of ulnar neuropathy, advancements in imaging have led to increased utilization of these newer / better imaging techniques in the overall management of ulnar neuropathy. Specifically, high-resolution ultrasonography of peripheral nerves as well as MRI has become quite useful in evaluating the ulnar nerve in order to better guide treatment. The caliber and fascicular pattern identified in the normal ulnar nerves are important distinguishing features from ulnar nerve pathology. The cubital tunnel within the elbow and Guyon's canal within the wrist are important sites to evaluate with respect to ulnar nerve compression. Both acute and chronic conditions resulting in deformity, trauma as well as inflammatory conditions may predispose certain patients to ulnar neuropathy. Granulomatous diseases as well as both neurogenic and non-neurogenic tumors can also potentially result in ulnar neuropathy. Tumors around the ulnar nerve can also lead to mass effect on the nerve, particularly in tight spaces like the aforementioned canals. Although high-resolution ultrasonography is a useful modality initially, particularly as it can be helpful for dynamic evaluation, MRI remains most reliable due to its higher resolution. Newer imaging techniques like sonoelastography and microneurography, as well as nerve-specific contrast agents, are currently being investigated for their usefulness and are not routinely being used currently.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".