The Effect of Simulation Training in Anesthesia on Student Operational Performance and Patient Safety
Bibliographic record
Abstract
A veterinary anesthesia simulated environment (VASE) with clinical scenarios has been integrated into the pre-clinical curriculum at Midwestern University College of Veterinary Medicine to simulate anesthesia of a live patient within a surgical suite. Although this modality was shown to significantly improve veterinary students' perceived preparedness to perform anesthesia on live patients, whether this would improve anesthesia competency in the actual clinical environment, described as operational performance, remained unclear. Our goal was to examine the relationship between anesthesia simulation training and student anesthesia operational performance. Anesthesia operational performance assessment of students was determined by quantifying critical event occurrences that negatively impacted patient safety during the anesthesia of 287 patients during students' initial surgical experience in 2015 and 2016. The relationship between total numbers of critical incidents to students having anesthesia simulation training was determined through evaluation of anesthesia records from 2015 and 2016, where students did not have anesthesia simulation training or they had pre-clinical training, respectively. Results showed a significant relationship between simulation training and critical incident occurrence, with a critical incident more likely to occur during patient anesthesia for students who did not experience pre-clinical anesthesia simulation training. Of the total critical incidents that occurred in the two-year study, 88% were in patients anesthetized by students who did not have simulation training. Our findings suggest that students who were given the opportunity to participate in anesthesia-focused simulations before a live-animal anesthesia encounter demonstrated significant improvements in anesthesia operational performance and improved patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".