Ultrasound Imaging Accurately Identifies the Lateral Femoral Cutaneous Nerve
Bibliographic record
Abstract
BACKGROUND: Anesthesia of the lateral femoral cutaneous nerve (LFCN) is useful in surgery involving the anterolateral thigh. We investigated the accuracy of ultrasound compared with anatomical landmarks in identifying the LFCN in human cadavers and volunteers. METHODS: Twenty cadavers were examined. A needle was inserted targeting the LFCN with ultrasound guidance and green dye was injected. A second needle was inserted using anatomical landmarks. The LFCN was identified by dissection, and coloring of the LFCN and needle positions were evaluated. A volunteer study with 10 individuals was performed. Transdermal nerve stimulation was used to identify the LFCN bilaterally. Its position was compared with marked positions identified in advance using ultrasound and anatomical landmarks. RESULTS: Sixteen of 19 needles inserted under ultrasound guidance in the cadavers were in contact with the LFCN. The median horizontal distance from the needle tip to the nerve was 0.0 mm (interquartile range [IQR], 0.0-0.0 mm). Only 1 of 19 needles inserted using anatomical landmarks was in contact with the LFCN. The median horizontal distance from the needle tip to the nerve was 18.0 mm (IQR, 11.0-23.0 mm). Sixteen of 20 marked positions made using ultrasound guidance corresponded to the identified LFCN in volunteers. The median horizontal distance from the pen-mark to the LFCN was 0.0 mm (IQR, 0.0-0.0 mm). None of the 20 marked positions made with anatomical landmarks corresponded to the LFCN. The median horizontal distance from the pen-mark to the LFCN was 15.0 mm (IQR, 10.8-20.0 mm). CONCLUSIONS: Identification of the LFCN by ultrasound is technically feasible and more accurate than anatomical landmarks.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".