Ultrasound Imaging Facilitates Spinal Anesthesia in Adults with Difficult Surface Anatomic Landmarks
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Bibliographic record
Abstract
BACKGROUND: Poor surface anatomic landmarks are highly predictive of technical difficulty in neuraxial blockade. The authors examined the use of ultrasound imaging to reduce this difficulty. METHODS: The authors recruited 120 orthopedic patients with one of the following: body mass index more than 35 kg/m² and poorly palpable spinous processes; moderate to severe lumbar scoliosis; or previous lumbar spine surgery. Patients were randomized to receive spinal anesthetic by the conventional surface landmark-guided technique (group LM) or by an ultrasound-guided technique (group US). Patients in group US had a preprocedural ultrasound scan to locate and mark a suitable needle insertion point. The primary outcome was the rate of successful dural puncture on the first needle insertion attempt. Normally distributed data were summarized as mean ± SD and nonnormally distributed data were summarized as median [interquartile range]. RESULTS: The first-attempt success rate was twice as high in group US than in group LM (65% vs. 32%; P < 0.001). There was a twofold difference between groups in the number of needle insertion attempts (group US, 1 [1-2] vs. group LM, 2 [1-4]; P < 0.001) and number of needle passes (group US, 6 [1-10] vs. group LM, 13 [5-21]; P = 0.003). More time was required to establish landmarks in group US (6.7 ± 3.1; group LM, 0.6 ± 0.5 min; P < 0.001), but this was partially offset by a shorter spinal anesthesia performance time (group US, 5.0 ± 4.9 vs. group LM, 7.3 ± 7.6 min; P = 0.038). Similar results were seen in subgroup analyses of patients with body mass index more than 35 kg/m and patients with poorly palpable landmarks. CONCLUSION: Preprocedural ultrasound imaging facilitates the performance of spinal anesthesia in the nonobstetric patient population with difficult anatomic landmarks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it