Visualization of the Long Thoracic Nerve using High-Resolution Sonography
Bibliographic record
Abstract
PURPOSE: The long thoracic nerve (LTN) innervates the serratus anterior muscle (SA) which plays an important role in shoulder function. Evaluation of the LTN has so far been restricted to clinical assessment and partly electromyography and neurography. Progress of high-resolution ultrasound (HRUS) increasingly enables visualization of small peripheral nerves and their pathologies. We therefore aimed at (a) clarifying the possibility of visualization of the LTN from its origin to the most distal point in the supraclavicular region visible and (b) developing an ultrasound protocol for routine use. We further present two cases of patients with LTN pathology. METHODS: The study consisted of two parts: Part 1 included 4 non-enbalmed human bodies in whom the LTN (n = 8) was located and then marked by ink injection. Correct identification was confirmed by anatomical dissection. Part 2 included 20 healthy volunteers whose LTN (n = 40) was assessed independently by two radiologists. Identification of the LTN was defined as consensus in recorded images. RESULTS: LTN was clearly visible in all anatomical specimens and volunteers using HRUS and could be followed until the second slip of the serratus anterior muscle from the supraclavicular region. In anatomical specimens, dissection confirmed HRUS findings. For all volunteers, consensus was obtained. The mean nerve diameter was 1.6 mm ± 0.3 (range 1.1 - 2.1 mm) after the formation of the main trunk. DISCUSSION: We hereby confirm a reliable possibility of visualization of the LTN in anatomical specimens as well as in volunteers. We encourage HRUS of the LTN to be part of the diagnostic work-up in patients presenting with scapular winging, shoulder weakness or pain of unknown origin.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".