Sonographic Visualization of the Posterior Cutaneous Nerve of the Forearm: Technique and Validation Using Perineural Injections in a Cadaveric Model
Bibliographic record
Abstract
OBJECTIVES: To determine the ability to sonographically identify the posterior cutaneous nerve of the forearm (PCNF) and its distal epicondylar branches using sonographically guided perineural injections in an unembalmed cadaveric model. METHODS: A single experienced operator used a 12-3-MHz linear array transducer to identify the PCNF and its distal epicondylar region branches in 10 unembalmed cadaveric specimens (6 right and 4 left) obtained from 10 donors. Sonographically guided perineural PCNF injections were then completed with a 22-gauge, 38-mm stainless steel needle to deliver 0.25 mL of 50% diluted colored latex at 3 points along the PCNF. The latex location was then confirmed via dissection. RESULTS: ). The operator sonographically identified the PCNF and several distal branches traversing over or directly adjacent to the lateral epicondyle in all 10 specimens. Only 7 of 10 specimens showed a distinct PCNF bifurcation into anterior and posterior divisions, and all 7 were accurately identified and localized on sonography. There was no evidence of latex overflow to clinically relevant adjacent structures or injury to regional vessels or nerves. CONCLUSIONS: High-resolution sonography can identify the PCNF and its distal epicondylar branches. Sonographic evaluation of the PCNF should be included in the evaluation of patients presenting with refractory or atypical lateral elbow pain syndromes. Diagnostic and therapeutic sonographically guided procedures targeting the PCNF or its lateral epicondylar branches are feasible and warrant further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".