Ultrasound-Guided Regional Anesthesia Performance in the Early Learning Period
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Success in performing ultrasound-guided peripheral nerve blockade (PNB) demands sound knowledge of sonoanatomy, good scanning techniques, and proper hand-eye coordination. The objectives of our study were to evaluate whether simulator training aids success of novice operators in ultrasound-guided PNB and to determine what number of procedures is required to attain proficiency. METHODS: Twenty Postgraduate Year 2 anesthesiology residents with no previous experience in ultrasound-guided PNB were randomly assigned into 2 groups. Both groups received conventional teaching comprising of 4 didactic lectures on PNBs with ultrasound guidance. Using a low-fidelity simulation model, 1 group further received an hourlong training session on needling and proper hand-eye coordination. Once the training was over, the residents started their rotation through our block room. Using a logbook, each resident recorded the number of successful and failed ultrasound-guided regional anesthesia blocks performed over a 3-week period. A successful block was defined as one that was effective for surgical anesthesia and performed within 15 mins, with only verbal guidance from a staff anesthesiologist. Cumulative summation charts were created to track progress using a predetermined acceptable failure rate of 30%. RESULTS: The conventional training group had 98 successful blocks, and the simulation group had 144 (51.3% vs 64%; P = 0.016). In the conventional training group, 4 of 10 residents achieved proficiency, and in the simulation training group, 8 of 10 residents achieved proficiency (80% vs 40%; P = 0.0849). CONCLUSIONS: Simulation training improves success rate in ultrasound-guided performance of regional anesthesia.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".