Human Patient Simulation Is Effective for Teaching Paramedic Students Endotracheal Intubation
Bibliographic record
Abstract
OBJECTIVES: The primary purpose of this study was to determine whether the endotracheal intubation (ETI) success rate is different among paramedic students trained on a human patient simulator versus on human subjects in the operating room (OR). METHODS: Paramedic students (n = 36) with no prior ETI training received identical didactic and mannequin teaching. After randomization, students were trained for ten hours on a patient simulator (SIM) or with 15 intubations on human subjects in the OR. All students then underwent a formalized test of 15 intubations in the OR. The primary outcome was the rate of successful intubation. Secondary outcomes were the success rate at first attempt and the complication rate. The study was powered to detect a 10% difference for the overall success rate (alpha = 0.05, beta = 0.20). RESULTS: The overall intubation success rate was 87.8% in the SIM group and 84.8% in the OR group (difference of 3.0% [95% confidence interval {CI} = -4.2% to 10.1%; p = 0.42]). The success rate on the first attempt was 84.4% in the SIM group and 80.0% in the OR group (difference of 4.4% [95% CI = -3.4% to 12.3%; p = 0.27]). The complication rate was 6.3% in the SIM group and 4.4% in the OR group (difference of 1.9% [95% CI = -2.9% to 6.6%; p = 0.44]). CONCLUSIONS: When tested in the OR, paramedic students who were trained in ETI on a simulator are as effective as students who trained on human subjects. The results support using simulators to teach ETI.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".