Mastering Sedation and Associated Respiratory Events through Simulation-Based Training: A Randomised Controlled Trial Involving Non-Anaesthesiology Residents
Bibliographic record
Abstract
Non-anaesthetists commonly administer procedural sedation worldwide, posing the risk of respiratory events that can lead to severe complications. This study aimed to evaluate whether simulation-based learning could lead to enhancements in the clinical proficiency of non-anaesthesiology residents in managing sedation and related respiratory complications. Following the evaluation of baseline clinical performance through a pre-test simulation, 34 residents were randomly allocated to either participate in an innovative simulation-based learning module (intervention group) or view a brief self-learning video (control group). After a one-month period, their clinical performance was assessed again in a post-test simulation involving respiratory arrest during procedural sedation. Two independent assessors rated each resident’s performance using video recordings and a scoring tool with scores ranging from 0 to 19/19. The two assessments were averaged for each performance, and the pre- to post-test change was calculated for each resident. While baseline clinical performance was similar, mean (SD) increase in clinical performance was significantly greater in the intervention group than in the control group (+2.4 (1.6) points versus +0.8 (1.3) points, respectively; p = 0.002). Our simulation-based learning sedation module resulted in the enhanced management of sedation-related complications compared to baseline and minimal self-learning. Simulation-based medical education may offer an effective approach for equipping non-anaesthesiology residents with essential skills to mitigate risks associated with sedation. (ClinicalTrials.gov identifier: NCT02722226).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".