Video Review of Simulated Pediatric Cardiac Arrest to Identify Errors/Latent Safety Threats: A Mixed Methods Study
Bibliographic record
Abstract
INTRODUCTION: Outcomes from pediatric in-hospital cardiac arrest depend on the treatment provided as well as resuscitation team performance. Our study aimed to identify errors occurring in this clinical context and develop an analytical framework to classify them. This analytical framework provided a better understanding of team performance, leading to improved patient outcomes. METHODS: We analyzed 25 video recordings of pediatric cardiac arrest simulations from the pediatric intensive care unit at the Alberta Children's Hospital. We conducted a qualitative-dominant crossover mixed method analysis to produce a broad understanding of the etiology of errors. Using qualitative framework analysis, we identified and qualitatively described errors and transformed the data coded into quantitative data to determine the frequency of errors. RESULTS: We identified 546 errors/error-related actions and behaviors and 25 near misses. The errors were coded into 21 codes that were organized into 5 main themes. Clinical task-related errors accounted for most errors (41.9%), followed by planning, and executing task-related errors (22.3%), distraction-related errors (18.7%), communication-related errors (10.1%), and knowledge/training-related errors (7%). CONCLUSIONS: This novel analytical framework can robustly identify, classify, and describe the root causes of errors within this complex clinical context. Future validation of this classification of errors and error-related actions and behaviors on larger samples of resuscitations from various contexts will allow for a better understanding of how errors can be mitigated to improve patient outcomes.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".