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Record W4407766561 · doi:10.1016/j.resplu.2025.100912

Defining the terminology of first responders alerted for out-of-hospital cardiac arrest by medical dispatch centres: An international consensus study on nomenclature

2025· article· en· W4407766561 on OpenAlexaff
Camilla Metelmann, Bibiana Metelmann, Michael Müller, Tommaso Scquizzato, Enrico Baldi, Tomás Barry, Bernd W. Böttiger, Hans-Jörg Busch, Maria Luce Caputo, Sheldon Cheskes, Ruggero Cresta, Charles D. Deakin, Eva Degraeuwe, Ankur Doshi, Mette M Ekkel, Daniel Elschenbroich, David Fredman, Lorenzo Gamberini, Julian Ganter, Finn Lund Henriksen, Caroline Jagtenberg, Martin Jönsson, Michael Khalemsky, Tom A Kooy, Carsten Lott, Tore Marks, Koenraad G. Monsieurs, Esther Moens, Wei Ming Ng, Jan‐Steffen Pooth, Stefan Prasse, David D. Salcido, Andrea Scapigliati, Nadja Schittko, Sebastian Schnaubelt, S. Scholz, Persia Shahriari, Paul Snobelen, Remy Stieglis, Bernd Strickmann, Hanno L. Tan, Karl‐Christian Thies, Steven Vercammen, Wolfgang A. Wetsch, Robert Greif

Bibliographic record

VenueResuscitation Plus · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Toronto
FundersEuropean Research CouncilADC FoundationTrygFondenDeutsche HerzstiftungEuropean Cooperation in Science and TechnologyDeutsche Stiftung für Herzforschung
KeywordsTerminologyNomenclatureMedicineConsensus conferenceMedical emergencyInternal medicineTaxonomy (biology)BiologyLinguistics

Abstract

fetched live from OpenAlex

Aim: Emergency medical services target to reduce time to cardiopulmonary resuscitation and defibrillation by alerting additional individuals to out-of-hospital cardiac arrest (OHCA). Multiple terms are used to describe these individuals, potentially causing confusion and hindering comparisons. This international consensus study aimed to establish standardised terminology. Methods: Forty-six interdisciplinary researchers from four continents participated in a symposium on "Community First Responders" with the objective of standardising relevant terminology. Initially, terms were proposed anonymously for individuals alerted during work hours and those alerted during leisure time. Each term was rated on a 5-point Likert scale. Terms receiving a high level of agreement were included in the final voting process. Results: Seven terms were suggested for individuals alerted during work hours. In the first voting "first responder", "professional first responder", and "on-duty first responder" achieved high agreement. Ultimately, consensus was reached on the term "on-duty first responder".For individuals alerted during leisure time, ten terms were proposed. Among these, "first responder", "citizen first responder", "community emergency responder", "community first responder", "volunteer first responder", "volunteer responder", and "volunteer community first responder" reached high agreement. In the final vote "community first responder" was selected.The consensus group agreed that the overarching term "first responder" should be used to describe all community-based individuals, who are alerted, regardless of whether they are on duty or off duty. Conclusion: This consensus study recommends using the terms "on-duty first responder" and "community first responder" to describe individuals additionally alerted by medical dispatch centres to facilitate early intervention in 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.230
metaresearch head score (Gemma)0.273
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2300.273
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0090.006
Science and technology studies0.0050.009
Scholarly communication0.0070.010
Open science0.0090.013
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.013
GPT teacher head0.316
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
GenreEmpirical

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".

Quick stats

Citations12
Published2025
Admission routes1
Has abstractyes

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