The Sensitivity of Motor Response to Nerve Stimulation and Paresthesia for Nerve Localization As Evaluated by Ultrasound
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Seeking paresthesia and obtaining a motor response to an electrical stimulus are the two most common methods of nerve localization for the performance of peripheral-nerve blocks. However, these two endpoints do not always correlate, and the actual sensitivity and specificity of either method remains unknown. The objective of this study is to determine the sensitivity of paresthesia and motor response to electrical nerve stimulation as tools for nerve localization when a 22-gauge insulated needle is used for the performance of axillary-nerve block. METHODS: After IRB approval and informed consent, 103 patients were enrolled. Real-time ultrasonography was used as the reference test. After needle-to-nerve contact was confirmed by ultrasonography, the patient was requested to report the presence of paresthesia, and a nerve stimulator was used to seek a motor response, with a stimulating current of 0.5 mA or less. RESULTS: One patient was excluded from analysis because of protocol violation. Paresthesia was found to be 38.2% sensitive and motor response was 74.5% sensitive for detection of needle-to-nerve contact. CONCLUSION: The very different and relatively low sensitivity of either technique may explain, in part, the lack of correlation previously reported between the 2 endpoints.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".