Sonography of the Lateral Antebrachial Cutaneous Nerve With Magnetic Resonance Imaging and Anatomic Correlation
Bibliographic record
Abstract
OBJECTIVES: Abnormalities of the lateral antebrachial cutaneous nerve (LABCN) are associated with antecubital elbow conditions, such as distal biceps brachii tendon tears and traumatic cephalic vein phlebotomy. These can lead to lateral forearm, elbow, and wrist symptoms that can mimic other disease processes. The purpose of this study was to characterize the sonographic appearance of the LABCN using cadaveric dissection and retrospective analysis of sonographic examinations of symptomatic patients with magnetic resonance imaging correlation. METHODS: For the first part of this study, a cadaveric elbow specimen was examined, and sonography was performed after dissection to identify the LABCN. Subsequently, 26 elbows in 13 patients with LABCN abnormalities were identified with sonography and retrospectively evaluated to characterize the appearance of the LABCN in both symptomatic and asymptomatic elbows. RESULTS: The symptomatic LABCNs showed fusiform enlargement, increased echogenicity, and loss of the normal fascicular echo texture. The mean cross-sectional area of the symptomatic nerves was 12.0 mm(2) (range, 6.1-17.2 mm(2)), with a maximum thickness of 3.5 mm (range, 2.3-5.9 mm), compared to 3.3 mm(2) (range, 1.9-5.2 mm(2)), with a maximum thickness of 1.3 mm (range, 0.9-2.2 mm), in the contralateral normal elbows. CONCLUSIONS: The close proximity of the LABCN to the distal biceps tendon and the cephalic vein makes it vulnerable to compression and injury in the setting of distal biceps tendon tears and traumatic phlebotomy, which may cause nerve enlargement and increased echogenicity. Awareness of the location and appearance of the LABCN on sonography is important for determining potential causes of lateral elbow and forearm pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".