Assessment of Canadian Public Automated External Defibrillator Registries
Bibliographic record
Abstract
BACKGROUND: Public automated external defibrillator (AED) registries aim to increase layperson defibrillation for victims of out-of-hospital cardiac arrest. This study aims to characterize Canadian AED registries and the process by which these databases are updated and used. METHODS: A survey was administered to representatives from each eligible AED registry. Collected data included information on registry management, AED validation process, linkage to emergency medical dispatch (EMD), and number of AEDs per registry. Three unregistered AEDs in each region were then located and registered into their respective registry. The primary endpoint was the proportion of AEDs that became visible in the registry within 1 month. RESULTS: Of the 9 Canadian provinces that have registries, 7 are provincial, whereas 2 contain smaller independent registries. The survey was completed by 90% of contacted registries. The number of AEDs per registry ranged from 21 to 443 per 100,000 persons. Six registries are managed by a provincial government, 6 use a standardized validation process, and 8 are linked to EMD. Of the 21 AEDs registered by our study personnel in 7/10 registries, 9 (43%) were made available to the public within 1 month of registration. Only 1 registry employed an AED validation process that included direct contact with AED managers. CONCLUSIONS: Canadian public AED registries demonstrate significant differences in their governance and administrative processes. A majority of registries are integrated with EMD for out-of-hospital cardiac arrest, but not all registries use a standardized validation process to ensure accuracy of AED information submitted by the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".