Construct Validity of Simulation Model for Training the Ultrasound-Guided Nerve Block and Catheter Placement
Bibliographic record
Abstract
Abstract Background: Continuous ultrasound-guided peripheral nerve block is a widely practiced technique that can be a challenge for young anesthesiologists. We developed a new simulation model for learning perineural catheter placement. This study aims to test the validity of the model and investigate the learning process of residents on this model. Methods: The simulation model was constructed with partial animal tissue and physical materials. Ultrasound-guided simulation of perineural catheter placement was then performed. Twenty-eight anesthesiologists representing novices, intermediates and experts were required to perform 30 trials within one single day. Practice trials were video-recorded to extract data of task time and performance errors. Construct validity were assessed. Results: Practicing on the simulation model, experts were able to achieve superior task performance, i.e., shorter task time (P < 0.001) and fewer performance errors (P < 0.001), than intermediates and novices. Learning curve recorded from young residents suggested that they were not able to reach the proficiency level as experts within one training day. Conclusions: This hybrid simulation model was capable of training the skills required for continuous ultrasound-guided peripheral nerve block. Future studies under this model need to concern extending the duration of the training phase for residents.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".