Ultrasound‐Guided Anterior Approach to a Sciatic Nerve Block
Bibliographic record
Abstract
OBJECTIVES: We aimed to identify the optimal lower limb position for an ultrasound (US)-guided anterior approach to a sciatic nerve block. METHODS: We included 45 patients who met the following criteria: American Society of Anesthesiologists physical status of 1 to 3, age between 18 and 80 years, and scheduled to undergo knee surgery that required a sciatic nerve block. The lower limbs of each patient were placed in the following 4 positions: N, neutral; ER, external rotation of the hip (angle, 45°); ER/F15, ER (angle, 45°) and flexion (angle, 15°) of the hip; and ER/F45, ER (angle, 45°) and F (angle, 45°) of the hip. An investigator acquired US scans of the sciatic nerve in each position, and the visibility score and depth of the sciatic nerve from the skin were analyzed. RESULTS: The visibility scores were significantly higher in positions ER/F15 and ER/F45 than in positions ER and N (P < .0001). However, there was no difference between the visibility scores in positions ER/F15 and ER/F45 (P = .0959). The depth of the sciatic nerve from the skin decreased with ER and an increase in the F angle of the hip (overall P < .0001). CONCLUSIONS: Based on the visibility score and depth from the skin, ER of the hip to 45° with a greater F angle (45° versus 15°) of the hip appears to be the optimal position for an US-guided anterior approach to a sciatic nerve block.
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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 | 0.000 | 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 | 0.000 |
| 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".