Immersive Video Modeling Versus Traditional Video Modeling for Teaching Central Venous Catheter Insertion to Medical Residents
Bibliographic record
Abstract
Video modeling (VM) is an instructional adjunct that improves the quality and success of CVC insertion. Immersive VM can improve recall and skill translation, but its role in teaching medical procedures is not established. Research question/hypothesis We hypothesized that, relative to traditional VM, immersive VM would decrease cognitive load and enhance ultrasound-guided CVC insertion skill acquisition. Methods Thirty-two resident physicians from four specialties were randomized into traditional (control) or immersive VM (intervention) groups for three CVC training sessions. Cognitive load was quantified via NASA Task Load Index (TLX). Mean (± standard deviations) values were compared using two-tailed t-tests. Skill acquisition was quantified by procedural time and the average 5-point [EM1] [TB2] entrustment score of three expert raters. Results Overall entrustment scores improved from the first (3.44±0.98) to the third (4.06±1.23; p<0.002) session but were not significantly different between the control and intervention groups. There were no significant differences between NASA TLX scores or procedural time. Conclusion We found no significant difference in entrustment, cognitive load, or procedural time. Immersive VM was not found to be superior to traditional VM for teaching CVC insertion.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".