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
Introduction: Millions of out-of-hospital cardiac arrests (OHCA) occur globally each year. Survival after OHCA can be improved with the use of automated external defibrillators (AED). The main strategy for facilitating bystander defibrillation has been fixed-location public access defibrillators (PADs). New strategies of mobile AEDs depart from the model of static PADs and have the potential to address known barriers to early defibrillation and improve outcomes. Methods: Mobile AEDs was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17, 2023, in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised, and ranked by all attendees to identify the top 5 for each category. Results: Top knowledge gaps center around understanding the impact of mobile AEDs on OHCA outcomes in various settings and the impact of novel AED technologies. Top barriers to translation include questionable public comfort/acceptance, financial/regulatory constraints, and a lack of centralized accountability. Top research priorities focus on understanding the impact of the mobile AED strategies and technologies on time to defibrillation and OHCA outcomes. Conclusion: This work informs research agendas, funding priorities and policy decisions around using mobile AEDs to optimize prehospital response to 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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