Data concerning AED registration in the Danish AED Network, and cardiac arrest-related characteristics of OHCAs, including AED coverage and AED accessibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The data presented in this article is supplemental data related to the research article entitled "Automated external defibrillator accessibility is crucial for bystander defibrillation and survival: a registry-based study" (Karlsson et al., 2019). We present detailed data concerning: 1) the type of location for deployed and registered automated external defibrillators (AEDs) in the nationwide Danish AED Network; 2) the number of registered AEDs in the nationwide Danish AED Network, and changes in AED registration (according to year and type of AED location); 3) the number of AEDs being withdrawn from the AED network between the years 2007-2016. We also report data on baseline cardiac arrest-related characteristics of out-of-hospital cardiac arrests (OHCAs) that occurred in Copenhagen, Denmark, between 2008 and 2016. Cardiac arrest-related characteristics are further described according to AED accessibility (accessible vs. inaccessible AED at the time of OHCA) for OHCAs covered by an AED (AED ≤200 m route distance of an OHCA). Finally, we report data on distance to the nearest accessible AED for bystander defibrillated OHCAs covered by an AED ≤200 m route distance where the AED was inaccessible at the time of OHCA.
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it