A Valid and Reliable Assessment Tool for Remote Simulation-Based Ultrasound-Guided Regional Anesthesia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The purpose of this study was to establish construct and concurrent validity and interrater reliability of an assessment tool for ultrasound-guided regional anesthesia (UGRA) performance on a high-fidelity simulation model. METHODS: Twenty participants were evaluated using a Checklist and Global Rating Scale designed for assessing any UGRA block. The participants performed an ultrasound-guided supraclavicular brachial plexus block on both a patient and a simulator. Evaluations were completed in-person by an expert and remotely by a blinded expert using video recordings. Using previous number of blocks performed as an indication of expertise, participants were divided into Novice (n = 8) and Experienced (n = 12) groups. Construct validity was assessed through the tool's reliable on-site and remote discrimination of Novice and Experienced anesthetists. Concurrent validity was established by comparisons of patient versus simulator scoring. Finally, interrater reliability was determined by comparing the scores of on-site and off-site evaluators. RESULTS: The Global Rating Scale was able to differentiate Novice from Experienced anesthetists both by on-site and remote assessment on a patient and simulation model. The Checklist was unable to discern the 2 groups on a simulation model remotely and was marginally significant with on-site scoring. CONCLUSIONS: This is the first study to demonstrate the validity and reliability of a Global Rating Scale assessment tool for use in UGRA simulation training. Although the checklist may require further refinement, the Global Rating Scale can be used for remote and on-site assessment of UGRA skills.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".