Reporting standard for describing first responder systems, smartphone alerting systems, and AED networks
Bibliographic record
Abstract
Standardized reporting of data is crucial for out-of-hospital cardiac arrest (OHCA) research. While the implementation of first responder systems dispatching volunteers to OHCA is encouraged, there is currently no uniform reporting standard for describing these systems. A steering committee established a literature search to identify experts in smartphone alerting systems. These international experts were invited to a conference held in Hinterzarten, Germany, with 40 researchers from 13 countries in attendance. Prior to the conference, participants submitted proposals for parameters to be included in the reporting standard. The conference comprised five workshops covering different aspects of smartphone alerting systems. Proposed parameters were discussed, clarified, and consensus was achieved using the Nominal Group Technique. Participants voted in a modified Delphi approach on including each category as a core or supplementary element in the reporting standard. Results were presented, and a writing group developed definitions for all categories and items, which were sent to participants for revision and final voting using LimeSurvey web-based software. The resulting reporting standard consists of 68 core items and 21 supplementary items grouped into five topics (first responder system, first responder network, technology/algorithm/strategies, reporting data, and automated external defibrillators (AED)). This proposed reporting standard generated by an expert opinion group fills the gap in describing first responder systems. Its adoption in future research will facilitate comparison of systems and research outcomes, enhancing the transfer of scientific findings to clinical practice.
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 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 | 0.444 | 0.644 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".