Crash testing the dummy: a review of in situ trauma simulation at a Canadian tertiary centre
Bibliographic record
Abstract
Background: In situ trauma simulations allow for the trauma team and emergency department to practise team dynamics, resuscitation and logistics in a safe environment. The goal of this investigation was to show the feasibility of an in situ trauma simulation program at a Canadian level 1 trauma centre. Methods: We performed a retrospective review of in situ simulations (maximum 20 min, followed by a 10-min debriefing session) at a level 1 trauma centre from 2015 to 2017. Errors were categorized according to the National Patient Safety Agency risk assessment matrix by 3 independent raters and assigned consequence scores (assessing potential harm) and likelihood scores (assessing the likelihood of potential harm). A risk score was calculated as the product of the mean consequence and likelihood scores. Errors per simulation and the number of simulations required for error resolution were recorded. Results: We reviewed 8 in situ simulations and identified 54 errors, of which 7 were related to medications, 20 to equipment, 21 to environment/staffing and 6 to training. The mean consequence score was 2.85/5 (standard deviation [SD] 0.75, intraclass correlation coefficient [ICC] 28%), indicating minor to moderate harm. The mean likelihood score was 2.82/5 (SD 0.55, ICC 41%), indicating unlikely to possible. The mean risk score was 8.42/25 (SD 3.19, ICC 43%). One error (2%) was low risk, 23 (43%) were moderate risk, 26 (48%) were high risk, and 4 (7%) were extreme risk. Conclusion: In situ trauma simulations are feasible in a Canadian centre and provide a safe environment to identify and rectify errors.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".