The Requisites of Needle-to-Nerve Proximity for Ultrasound-Guided Regional Anesthesia
Bibliographic record
Abstract
This scoping review examines the literature to determine whether the position of the needle tip relative to the target nerve is accurately and reliably detected during ultrasound (US)–guided regional anesthesia. The requisites for successful and safe needle tip positioning relative to the target nerve include accurate and reliable needle presentation by the machine, needle interpretation by the operator, nerve presentation by the machine, and nerve interpretation by the operator. Failure to visualize the needle tip is a common occurrence, frequently prompting operators to use needle and probe maneuvers, which are not necessarily based on evidence. Needle tip interpretation often relies on surrogate indicators that have not been validated. The acoustic resolution of modern portable US machines limits the extent to which nerve microanatomy can be reliably presented. Finally, our interpretation of the sonographic end points for local anesthetic injection that best balance success and safety for US-guided regional anesthesia continues to evolve. What9s New In order to determine whether or not the position of the needle tip relative to the target nerve is accurately and reliably detected during US-guided regional anesthesia, the available literature is reviewed and interpreted to address the following 4 questions: Is the presentation of needle tip by the ultrasound machine accurate and reliable? Is the interpretation of the needle tip image by the operator accurate and reliable? Is the presentation of the nerve by the ultrasound machine accurate and reliable? Is the interpretation of the nerve image by the operator accurate and reliable?
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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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".