Interdisciplinary development and evaluation of a novel needle guide for ultrasound-guided lumbar epidural placement
Bibliographic record
Abstract
Purpose A solution for real-time, ultrasound-guided, central neuraxial blockade placement remains elusive. A device that enables single-operator neuraxial placement while simultaneously visualising the spinal anatomy and needle trajectory may improve patient safety. We engineered a novel needle guide, the EpiGuide two dimensional (2D), and compared prepuncture insertion sites as located using the guide versus standard manual palpation. Methods Interdisciplinary collaboration between engineers and obstetric anaesthesiologists and multiple iterative refinement led to the EpiGuide 2D, a prototype multichannel needle guide for ultrasound transducers. Following ethics committee approval, 22 healthy adult participants were recruited to undergo prepuncture lumbar epidural placement using the guide. The primary outcome was accuracy, defined as the percentage of successful placements of prepuncture needle insertion sites within the manually palpated intervertebral space. The secondary outcome was distance between the prepuncture insertion sites guided by the EpiGuide 2D versus by sites guided by manually palpation. Results Mean (SD) body mass index of participants was 22.6 (2.1) kg/m2. The success rate of selecting a prepuncture site within the manually palpated intervertebral space using EpiGuide 2D was 95.5% (42 of 44 attempts). Of the failed sites, the mean (SD) distance to the intervertebral space was 1.3 (0.3) mm in the caudal direction. The mean (SD) distance between EpiGuide 2D sites and palpated sites was 3.6 (2.0) mm. Conclusion The EpiGuide 2D, jointly developed between engineers and anaesthesiologists, was found to be as accurate as manual palpation in placing a prepuncture site within the intervertebral space.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".