Discriminative Ability of Dye Injected Into a Meat Model to Determine Accuracy of Ultrasound-Guided Injection
Bibliographic record
Abstract
INTRODUCTION: The utility of using meat models for ultrasound-guided regional anesthesia simulation training has been well established. Feedback is considered the most important element of successful simulation-based education, and simulation offers an opportunity for evaluation. The objective of this study was to establish the discriminative ability of dye injected into a meat model to determine whether injectate is properly placed in the perineural (PN) space, thus providing an additional tool for learner feedback and evaluation. METHODS: Meat models containing a beef tendon (simulating a nerve) were injected with dye in one of 3 locations: PN, intraneural, and intramuscular. Blinded assessors then independently interpreted the dye staining on the models, marked the interpreted injection location, ease of interpretation, and whether staining was present on the beef tendon. RESULTS: Thirty meat models were injected with dye and independently assessed. Determining the location of injection was deemed to be easy or very easy in 72% of the models. Assessors correctly identified PN, intraneural, and intramuscular injections 100%, 95%, and 85% of the time, respectively. Assessor agreement was 87%. CONCLUSIONS: The location of dye injected into a meat model, simulating a peripheral nerve blockade, can be accurately and reliably scored to provide feedback to learners. This technique offers a novel means of providing feedback to trainees and assessing block success in ultrasound-guided regional anesthesia simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".