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Record W3108542236 · doi:10.1093/ehjci/ehaa946.1856

Association of chain of survival factors with out of hospital cardiac arrest survival in a region with low average population-density: a retrospective population-based cohort study

2020· article· en· W3108542236 on OpenAlexaffabout
Michael S. Connolly, Judah Goldstein, Karen Giddens, M Nallbani, Peter T. Kennedy, Margaret Currie, A Carter, A. Travers, James H. Sapp

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicinePopulationRetrospective cohort studyCohortSurvival analysisEmergency medicineDemographyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Out of hospital cardiac arrest (OHCA) has an average global survival rate to discharge of 8%. Chain of survival factors are heavily time-dependant and optimization can increase survival. Regions with low population density encounter challeges in providing optimal OHCA care. Nova Scotia's average population density is 17.4 persons per square kilometer in compasiron to Toronto with 4334.4 person per square kilometer. OHCAs have been described well in large urban centers globally, however the characterization of OHCA chain of survival in low density populations is sparse. Purpose To describe chain of survival factors and identify characteristics of survivors and non-survivors among those treated by paramedics in a low average density provincial population. Methods This was a retrospective cohort study of OHCAs responded to by paramedics. All OHCA responses with a cardiac etiology in Nova Scotia, Canada were included. Exclusion criteria were non-cardiac cause arrests, those with “do not resuscitate” (DNR) directives and expected deaths. The paramedic electronic patient care record was reviewed for demographic, bystander, out of hospital treatment and operational characteristics. Primary outcome was survival to hospital discharge. Descriptive statistics were calculated to describe differences between survivorship using Prism 8.0 (San Diego, CA) with alpha=0.05 applying unpaired, Mann-Whitney tests. Results Of 1517 OHCA, 463 were excluded leaving 1054 OHCA. Of these, 478 (45.3%) were treated by paramedics and included in this analysis. Most were men (67.2%; n=274) with a mean age 66.8 (±16.4). A total of 7.1% (n=75) survived to discharge with 76% of survivors (n=58) discharged home. Survivors were more likely to present with ventricular fibrillation than non-survivors (42.7% vs. 19.6%). Survivors compared to non-survivors had significantly shorter paramedic response time (8.1 vs. 10.7 min, P<0.001), paramedic time on scene (35.7 vs. 45.4 min, P=0.002), estimated time to paramedic defibrillation (13.2 vs 19.4 min, P<0.001), and estimated time to return of spontaneous circulation (ROSC) (22.9 vs 31.9min, P<0.001). Conclusion Links in the chain of survival are associated with survival from OHCA. OHCA survival is lower in the less densely populated province of Nova Scotia compared to studies in urban Canadian centers and worldwide. Our study is limited by the retrospective nature of data collection and lack of access to neurological outcomes. Even among survivors, EMS response is delayed compared to more densely populated centers. In Nova Scotia, longer paramedic response times are associated with decreased survival. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Maritime Heart Center

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)0.002
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 score0.253
Threshold uncertainty score0.502

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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