Feasibility of 3D Ultrasound to Evaluate Upper Extremity Nerves
Bibliographic record
Abstract
PURPOSE: This study investigates the performance of a 3 D Ultrasound (US) system in imaging elbow and wrist nerves. MATERIALS AND METHODS: Twenty healthy volunteers with asymptomatic median, ulnar and radial nerves were prospectively investigated. Bilateral 3DUS scans of the elbows and wrists were acquired by using a commercially available US scanner (18 MHz, AplioXG, Toshiba) and stored as a 3 D volume by a dedicated software (CURE, Robarts Research Institute). Retrospectively, qualitative (image quality, atypical nerve location, findings potentially associated with compression neuropathy) and quantitative (cross-sectional area measurements) evaluations were performed. RESULTS: In all 200 nerves 3DUS was feasible (100%). Image quality was insufficient in 13.5% (25 ulnar nerve elbow, 2 radial nerve) and sonomorphology was not assessable in those nerves. Measurement of cross sectional areas was feasible in all nerves (100%). Median cross-sectional area (range) were: median nerve elbow 7 mm2 (6-9), radial nerve 3 mm2 (1-4), ulnar nerve elbow 8 mm2 (5-11), median nerve wrist 8 mm2 (5-10), and ulnar nerve wrist 4 mm2 (2-6). No significant changes in nerve cross-sectional area along each nerve was found. Ulnar nerve subluxation was found in 2 nerves (6.7%). No anconeus epitrochlearis muscle or osteophytes were found. CONCLUSION: 3DUS is a feasible method for assessing nerves of the upper extremity and has been shown to provide a good overview of the median, ulnar and radial nerve at the elbow and wrist, but is limited for evaluation of the ulnar nerve in the cubital tunnel. This technique enables reliable measurements at different locations along the nerve.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".