Echotexture and Correlated Histologic Analysis of Peripheral Nerves Important in Regional Anesthesia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Peripheral nerves in different body locations display different echotextures on ultrasound imaging, and knowledge of peripheral nerve echotexture is helpful for locating target nerves. However, the degree of echogenicity is often difficult to characterize. We aimed to define objectively the degree of echogenicity of peripheral nerves using grayscale measurements and compare nerve echotexture with matched histologic samples. METHODS: Ultrasound images of peripheral nerves in 12 body locations were obtained in 20 healthy subjects using linear 8- to 12-MHz and curved 3- to 5-MHZ transducers. Corresponding nerve segments from 2 cadavers were imaged in vitro before they were sectioned for histologic examination. Nerve echogenicity was assessed by an objective grayscale (G) and a subjective echogenicity index (SEI) determined by experienced evaluators. The results of G and SEI in selected peripheral nerves were compared and correlated with histologic morphometry. RESULTS: There is a close correlation between SEI and G (P < 0.05). Mixed echogenicity was seen in 30% of the peripheral nerves; 25.4% were predominantly hypoechogenic, and 44.5% hyperechogenic. Nerves in the neck and upper arm are more frequently hypoechoic, whereas those in the leg are more frequently hyperechoic. Histologically, differences in echogenicity are dependent on fascicle diameter and on nerve fascicular pattern, that is, differing ratios of fascicle number to total nerve area. CONCLUSIONS: This study suggests that grayscales can be used to objectively determine echogenicity and shows that grayscale measurements match well with subjective visual grading. Histologic analysis showed that both ratio of total fascicular area to whole nerve area and fascicular pattern are important determinants of echogenicity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".