Correlation Among Ultrasound, Cross-Sectional Anatomy, and Histology of the Sciatic Nerve
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Efficient identification of the sciatic nerve (SN) requires a thorough knowledge of its topography in relation to the surrounding structures. Anatomic cross sections in similar oblique planes as observed during SN ultrasonography are lacking. A survey of sonoanatomy matched with ultrasound views of the major SN block sites will be helpful in pattern recognition, especially when combined with images that show the internal architecture of the nerve. METHODS: From 1 cadaver, consecutive parts of the upper leg corresponding to the 4 major blocks sites were sectioned and deeply frozen. Using cryomicrotomy, consecutive transverse sections were acquired and photographed at 78-microm intervals, along with histologic sections at 5-mm intervals. Multiplanar reformatting was done to reconstruct the optimal planes for an accurate comparison of ultrasonography and gross anatomy. The anatomic and histologic images were matched with ultrasound images that were obtained from 2 healthy volunteers. RESULTS: By simulating the exact position and angulation as in the ultrasonographic images, detailed anatomic overviews of SN and adjacent structures were reconstructed in the gluteal, subgluteal, midfemoral, and popliteal regions. Throughout its trajectory, SN contains numerous fascicles with connective and adipose tissues. CONCLUSIONS: In this study, we provide an optimal matching between histology, anatomic cross sections, and short-axis ultrasound images of SN. Reconstructing ultrasonographic planes with this high-resolution digitized anatomy not only enables an overview but also shows detailed views of the architecture of internal SN. The undulating course of the nerve fascicles within SN may explain its varying echogenic appearance during probe manipulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".