Safety Audits in the Emergency Department: Applying the Threat and Error Model to the Management of Pediatric Diabetic Ketoacidosis.
Bibliographic record
Abstract
OBJECTIVES: This study aims to better understand factors that impact management of patients with diabetic ketoacidosis (DKA) in the pediatric emergency department (ED) by novel application of the threat-and-error model, commonly used in the aviation industry. METHODS: This study was a retrospective chart review of all patients diagnosed with DKA and managed in our pediatric ED during a 1-year period. A "flight plan" was created for each patient's ED visit, from triage to final disposition. Each flight was analyzed with the goal of identifying threats and errors that may impact patients' clinical status or management. Particular focus was placed on physicians' adherence to hospital and provincial DKA protocols. Unintended patient states or outcomes were also noted. RESULTS: A total of 46 patient flights were outlined and analyzed. A total of 146 threats were identified, affecting 43 (93%) patient flights. No flight was error-free. Errors in communication and lack of adherence to protocol were the most common types of errors. Unintended patient states occurred in 30 cases (65%), some of which were preceded by at least 1 error. There were no cases of cerebral edema or death. CONCLUSIONS: It is important to identify and appropriately mitigate threats and errors that commonly occur during initial management of DKA in the ED to prevent unintended states and patient morbidity. This study demonstrates the threat-and-error model as a potentially useful tool for focusing quality improvement initiatives in the pediatric ED setting.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".