Ultrasound guidance in peripheral regional anesthesia: philosophy, evidence-based medicine, and techniques
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article introduces the use of ultrasound to facilitate peripheral regional anesthesia. RECENT FINDINGS: Regional anesthesia, despite its well known clinical benefits, has not gained the popularity of general anesthesia. This is secondary to multiple shortcomings including a defined failure rate, lack of simplicity, and the potential for patient discomfort or injury. Many of the negative aspects of regional anesthesia evolve from the reality that current nerve-localization techniques are unreliable. Given the great variation in human anatomy it is not surprising that even the most veteran clinician can be challenged by techniques that demand anatomical assumptions. The recent use of ultrasound imaging for nerve localization is an innovative application of an old technology which addresses many of the shortcomings of current techniques. Specifically, ultrasound imaging allows the operator to see neural structures, guide the needle under real-time visualization, navigate away from sensitive anatomy, and monitor the spread of local anesthetic. SUMMARY: Ultrasound technology represents an ideal mechanism by which the regional anesthesiologist can attain the safety, speed, and efficacy of general anesthesia. Ultimately, it is the correct peri-neural spread of local anesthetic around a nerve that provides safe, effective, and efficient anesthetic conditions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".