Use of the Virtual Ventilator, a Screen-Based Computer Simulation, to Teach the Principles of Mechanical Ventilation
Bibliographic record
Abstract
Examination scores from 109 students enrolled in the professional veterinary program at Washington State University were evaluated to determine the effectiveness and utility of the Virtual Ventilator computer simulation for teaching the principles of mechanical ventilation in an anesthesia course. Students were randomly assigned to either a live-animal mechanical ventilation laboratory (LIVE-1st) or a computer laboratory using the mechanical ventilation simulation (SIM-1st) in week 1. During week 2, students in the LIVE-1st group participated in the ventilation simulation while students in the SIM-1st group participated in the live-animal laboratory. Student knowledge was evaluated using two similar written quizzes administered following each laboratory. Student opinions concerning the value of the simulation were assessed using an online survey. Differences in quiz scores within and between groups were compared using t-tests while survey results were tabulated. A p value of less than 0.05 was considered significant. Within the LIVE-1st group, scores for the second quiz, which was taken after the students had completed the simulation exercise, were significantly higher than those obtained from the first quiz. Accordingly, the Virtual Ventilator simulation was at least equivalent to the live-animal laboratory in the ability to present information that was subsequently tested for on the quizzes. Students in the SIM-1st group reported that use of the simulation prior to a live-animal ventilation laboratory enhanced their understanding of and ability to provide mechanical ventilation to anesthetized patients. The Virtual Ventilator simulation appears to be a useful and well-received teaching tool.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".