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Bystander cardiopulmonary resuscitation and automated external defibrillator use after out-of-hospital cardiac arrest: uncovering differences in care and survival across the urban-rural spectrum

2021· article· en· W3213048405 on OpenAlexaff
Nicholas Grubic, Yingwei Peng, Melanie Walker, Steven C. Brooks

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationRural areaEmergency medicineLogistic regressionAutomated external defibrillatorBystander effectPsychological interventionDefibrillationChain of survivalResuscitationMedical emergencyInternal medicineBasic life supportPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Despite regional variation in survival following out-of-hospital cardiac arrest (OHCA), few studies have investigated urban-rural differences in the provision of care and outcomes after OHCA. To better understand the role of pre-hospital care across the urban-rural spectrum, we compared the effects of bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use on survival after OHCA between geographical settings. Methods This retrospective study (2013–2019) used all adult, non-traumatic, and treated OHCAs registered in the Cardiac Arrest Registry to Enhance Survival. The urban/rural status of arrest locations were classified at the census tract level as urban, suburban, large rural, small town, or rural, using the Rural-Urban Commuting Area classification system (Figure). Bystander interventions were grouped into three categories, including no bystander intervention, bystander CPR alone, and bystander AED use (with CPR). The primary outcome of interest was survival to hospital discharge with good neurological outcome. Multivariable logistic regression models were developed to assess the association between bystander interventions and survival with good neurological outcome by urban/rural status, adjusting for relevant covariates. Results A total of 325,281 patients were included. Bystander CPR alone occurred most often in rural areas (50.8%), and least often in urban areas (35.4%). Bystander AED use varied by urban/rural status (1.7%-2.9%), with large rural (2.9%) and rural areas (2.4%) reporting the highest rates. Survival to hospital discharge with good neurological outcome differed for urban (8.1%), suburban (7.7%), large rural (9.1%), small town (7.1%), and rural areas (6.1%). In all areas, patients who received bystander AED use or bystander CPR alone were more likely to achieve survival with good neurological outcome than patients who received no bystander intervention. The effect of bystander AED use on survival was stronger than bystander CPR alone in urban, suburban, and rural areas (no overlap of confidence bands), whereas no significant differences between these two bystander intervention groups were observed in large rural areas or small towns (overlap of confidence bands) (Table). Conclusions Bystander CPR and AED use are critical components of the response to OHCA across the urban-rural spectrum. The relative impact of bystander interventions on survival varied based on the geographical location of arrests, despite adjusting for numerous potential confounding variables, such as response time. It is possible that unmeasured factors, such as time from collapse to bystander intervention, patient factors, AED accessibility, and CPR quality are contributing to these observed differences. Future research is needed to better understand the response to OHCA across the urban-rural spectrum, which may inform policies for community-specific emergency protocols and planning. Funding Acknowledgement Type of funding sources: None.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.275
Teacher spread0.257 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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".

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Citations3
Published2021
Admission routes1
Has abstractyes

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