The AED Project: Multiorganization Collaboration to Streamline Automatic External Defibrillator Data in Out-of-Hospital Cardiac Arrests
Bibliographic record
Abstract
BACKGROUND: In patients with out-of-hospital cardiac arrest (OHCA), automated external defibrillator (AED) devices contain valuable data about the patient's initial rhythm. The retrieval process was previously without protocol, despite its critical role in the patient journey. METHODS: Through a Plan-Do-Study-Act model, the cardiology department at Royal Jubilee Hospital (Victoria, British Columbia, Canada) collaborated with provincial emergency health services (British Columbia Emergency Health Services) to cocreate a request process for data from AEDs used by first responders. British Columbia Fire Departments, which are under municipal oversight, required an alternate strategy. Educational presentations allowed for feedback and spread. Patients surviving OHCA and transfer to the regional cardiac centre were consecutively enrolled from November 2018 to April 2020. We evaluated the timeliness of AED information retrieval, and tracked the process to admission. A retrospective chart review informed specifics after admission. A survey to the Coronary Intensive Care Unit staff was used to assess clinical utility. RESULTS: Seventy-one consecutive patients were enrolled during the study period. Seven rhythm strips arrived with the patient, thus not affected by the initiative. From the remaining 64 cases, 80% (n = 51/64) were received within 48 hours, and 88% (n = 45/51) were received within 24 hours with a median of 1 hour. Eighteen Coronary Intensive Care Unit staff completed the survey; 81% reported AED data as "very useful" to clinical decision-making (n = 13/16). The AED rhythm strips provided insight into OHCA etiology (100%; n = 11/11), supported evidence for diagnoses (100%; n = 11/11), and reduced unnecessary testing (64%; n = 7/11). CONCLUSIONS: Implementing an organized protocol allowed for timely access to AED data, which was directly integrated into clinical decision-making and positively affected hospital stay.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".