Ultrasound Imaging of the Spine for Central Neuraxial Blockade: a Technical Description and Evidence Update
Bibliographic record
Abstract
Abstract Purpose of Review This article describes the anatomy of the spine, relevant ultrasonographic views, and the techniques used to perform the neuraxial blocks using ultrasound imaging. Finally, we review the available evidence for the use of ultrasound imaging to perform neuraxial blocks. Recent Findings Central neuraxial blockade using traditional landmark palpation is a reliable technique to provide surgical anesthesia and postoperative analgesia. However, factors like obesity, spinal deformity, and previous spine surgery can make the procedure challenging. The use of ultrasound imaging has been shown to assist in these scenarios. Summary Preprocedural imaging minimizes the technical difficulty of spinal and epidural placement with fewer needle passes and skin punctures. It helps to accurately identify the midline, vertebral level, interlaminar space, and can predict the depth to the epidural and intrathecal spaces. By providing information about the best angle and direction of approach, in addition to the depth, ultrasound imaging allows planning an ideal trajectory for a successful block. These benefits are most noticeable when expert operators carry out the ultrasound examination and for patients with predicted difficult spinal anatomy. Recent evidence suggests that pre-procedural neuraxial ultrasound imaging may reduce complications such as vascular puncture, headache, and backache. Neuraxial ultrasound imaging should be in the skill set of every anesthesiologist who routinely performs lumbar or thoracic neuraxial blockade. We recommend using preprocedural neuraxial imaging routinely to acquire and maintain the imaging skills to enable success for challenging neuraxial procedures.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".