Real‐time ultrasound‐guided spinal anesthesia using the <scp>SonixGPS</scp> ultrasound guidance system: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Real-time ultrasound-guided neuraxial blockade remains a largely experimental technique. SonixGPS® is a new needle tracking system that displays needle tip position on the ultrasound screen. We investigated if this novel technology might aid performance of real-time ultrasound-guided spinal anesthesia. METHODS: Twenty patients with body mass index < 35 kg/m(2) undergoing elective total joint arthroplasty under spinal anesthesia were recruited. Patients with previous back surgery and spinal abnormalities were excluded. Following a pre-procedural ultrasound scan, a 17G proprietary needle-sensor assembly was inserted in-plane to the transducer in four patients and out-of-plane in 16 patients. In both approaches, the trajectory of insertion was adjusted in real-time until the needle tip lay just superficial to the ligamentum flavum-dura mater complex. At this point, a 25G 120 mm Whitacre spinal needle was inserted through the 17G SonixGPS® needle. Successful dural puncture was confirmed by backflow of cerebrospinal fluid from the spinal needle. RESULTS: An overall success rate of 14/20 (70%) was seen with two failures (50%) and four failures (25%) in the in-plane and out-of-plane groups respectively. Dural puncture was successful on the first skin puncture in 71% of patients and in a single needle pass in 57% of patients. The median total procedure time was 16.4 and 11.1 min in the in-plane and out-of-plane groups respectively. CONCLUSION: The SonixGPS® system simplifies real-time ultrasound-guided spinal anesthesia to a large extent, especially the out-of-plane approach. Nevertheless, it remains a complex multi-step procedure that requires time, specialized equipment, and a working knowledge of spinal sonoanatomy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".