Implementing the Safe Airway Checklist (SAC) at the emergency department of a major teaching hospital in Rwanda: A pre- and post-intervention study
Bibliographic record
Abstract
Background: Airway management is a critical aspect of emergency care, and adherence to standardized protocols can improve patient outcomes. However, in resource-limited settings such as Rwanda, the implementation of airway management protocols in the emergency department (ED) may face challenges. This study aims to evaluate the impact of implementing the Safe Airway Checklist (SAC) on airway management practices and post-intubation complications in a major teaching hospital in Rwanda. Methods: A pre- and post-intervention study design was used to assess the impact of the SAC on intubation practices and post-intubation checklist in the ED at the University Teaching Hospital of Kigali. The study included a baseline assessment of residents' intubation practices, followed by implementation of the SAC, and post-implementation data collection to evaluate changes in adherence to airway management practices and post-intubation complications. Results: Among 77 intubation (40 pre-intervention and 37 post-intervention), the implementation of the SAC led to improvement in 4 key airway management practices (airway cart and glidescope setup, premedication use, restraining patients, and checking ABG within 10-15 min) in the ED. However, the reduction in rates of post-intubation complications was not statistically significant. Conclusion: The implementation of the Safe Airway Checklist in the ED of a major teaching hospital in Rwanda significantly improved several critical aspects of airway management. While no statistically significant reduction in post-intubation complications were observed, the decreasing trend of complication rates suggests promising benefits that merit further exploration. These findings highlight the value of standardized checklists in enhancing clinical practices and underscore the need for ongoing research to fully understand their impact on patient outcomes especially in low resources settings.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".