Implementing Automated Prone Ventilation for Acute Respiratory Distress Syndrome via Simulation-Based Training
Bibliographic record
Abstract
BACKGROUND: Prone position ventilation (PPV) is recommended for patients with severe acute respiratory distress syndrome, but it remains underused. Interprofessional simulation-based training for PPV has not been described. OBJECTIVES: To evaluate the impact of a novel interprofessional simulation-based training program on providers' perception of and comfort with PPV and the program's ability to help identify unrecognized safety issues ("latent safety threats") before implementation. METHODS: A prospective observational quality improvement study was done in the medical intensive care unit of an academic medical center. Registered nurses, physicians, and respiratory therapists were trained via a didactic session, simulations, and structured debriefings during which latent safety threats were identified. Participants completed anonymous surveys before and after training. RESULTS: A total of 73 providers (37 nurses, 18 physicians, 18 respiratory therapists) underwent training and completed surveys. Before training, only 39% of nurses agreed that PPV would be beneficial to patients with severe acute respiratory distress syndrome, compared with 96% of physicians and 70% of respiratory therapists (P < .001). Less than half of both nurses and physicians felt comfortable taking care of prone patients. After training, perceived benefit increased among all providers. Comfort taking care of proned patients and managing cardiac arrest increased significantly among nurses and physicians. Twenty novel latent safety threats were identified. CONCLUSION: Interprofessional simulation-based training may improve providers' perception of and comfort with PPV and can help identify latent safety threats before implementation.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".