Facilitating Needle Alignment In-Plane to an Ultrasound Beam Using a Portable Laser Unit
Bibliographic record
Abstract
OBJECTIVE: Ultrasound guidance can increase success with peripheral nerve blocks. Accurate anesthetic injection is optimized with both clear visualization and fine adjustment of the needle tip at the target area. Good needle alignment with the ultrasound beam and using a freehand technique are both desirable for these conditions. The purpose of this report is to describe how a unique, in-plane laser guide may be used to improve the alignment of injection needles with ultrasound beams in order to promote best needle tip visualization. ILLUSTRATION IMAGES: By using a small, battery-operated laser unit mounted onto an ultrasound transducer, a method to align the ultrasound scanning plane and laser-line projection plane was developed. Such alignment was further demonstrated and illustrated in a water bath model. Ultrasound was then used to show how clearly the needle shaft and tip can be visualized after needle alignment to the ultrasound beam using the laser line shining on the shaft of the needle. CONCLUSION: This in vitro demonstration describes the potential use of a readily available laser-line unit to assist with in-plane needle alignment with the ultrasound plane in order to ultimately improve needle visibility during ultrasound-guided peripheral nerve block. It requires minimum specialized training and may allow for maximum flexibility with freehand needle insertions in a sterile fashion.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".