Ultrasound Examination of Peripheral Nerves in the Forearm
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We examined in a volunteer population whether nerves in the forearm could be seen consistently using ultrasound imaging and whether this new information could have implications for the way we perform regional anesthesia of the median, radial, and ulnar nerves. METHODS: Eleven volunteers underwent ultrasound examination of both forearms. The median, ulnar, and radial nerves were followed and images were obtained at the elbow, proximal forearm, mid forearm, distal forearm and wrist levels. In addition the radial nerve was followed proximally to a point 5 cm above the elbow. Images were compared for consistency of location of the nerves and depth from skin and width was calculated for each nerve at each level. RESULTS: Anatomy of each nerve was consistent except for one forearm where the median nerve was lateral to the brachial artery at the elbow and one forearm where a superficial ulnar artery only joined the ulnar nerve at the wrist. A convenient location for blockade of both median and ulnar nerves is the midforearm combining ease of visualization, ability to block all terminal branches and minimal potential for vascular injury. The radial nerve is seen most easily at the elbow although blockade of the superficial radial nerve may spare radial motor function. CONCLUSIONS: Nerves in the forearm are consistently located using ultrasound. Further confirmation in clinical practice is required.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".