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Record W3104126375 · doi:10.1161/circ.142.suppl_4.306

Abstract 306: Out-of-hospital Cardiac Arrest Response Characteristics Moderate the Effect of Response Time on Survival

2020· article· en· W3104126375 on OpenAlexaff
Justin J. Boutilier, Clara Stoesser, Christopher Sun, Steven C. Brooks, Sheldon Cheskes, Katie N. Dainty, Michael J. Feldman, Dennis T. Ko, Steve Lin, Laurie J. Morrison, Damon C. Scales, Timothy C. Y. Chan

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSunnybrook Health Science CentreNorth York General HospitalQueen's UniversitySunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineBystander effectLogistic regressionCardiopulmonary resuscitationOddsOdds ratioResuscitationSurvival analysisShock (circulatory)Emergency medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Background: Research has shown that each minute delay in response time reduces survival from OHCA. Although Utstein variables like public location, witnessed, bystander CPR, and bystander AED shock are known to independently improve survival, how they moderate the effect of response time delays on survival is unknown. Methods: We included OHCAs from the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database from December 1, 2005 to June 30, 2015. We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCA episodes. We used a logistic regression model to estimate survival to hospital discharge as a function of response time. We adjusted for standard Utstein variables and included interaction terms between response time and public location, bystander witnessed, bystander CPR, and bystander AED shock. With four binary interacted variables, there were a total of sixteen subpopulations, each with a different effect of response time on survival. Results: 83,275 patients were included (15% public, 45% witnessed, 47% CPR, 2% AED shock). Across the 10 subpopulations that comprise 99%+ of the data, a one-minute delay in response time reduced the odds of survival from 1.7% to 10.9%, depending on the arrest characteristics. All interaction tests for effect modification were significant. The reduction in odds of survival was largest for witnessed arrests (OR=0.961; 95% CI: 0.944-0.978), followed by arrests with bystander CPR (OR=0.965; 95% CI: 0.948-0.982) and in public locations (OR=0.978; 95% CI: 0.960-0.996). In contrast, a one-minute delay for arrests with bystander AED shock (OR=1.086; 95% CI: 1.058-1.114) increased the odds of survival. Conclusions: Utstein predictors significantly moderate the effect of response time on survival. Arrests that are witnessed, public location, and/or receive bystander CPR are negatively affected by slower response time. Arrests with a bystander AED shock are not sensitive to response time delays.

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)0.008
metaresearch head score (Gemma)0.033
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 score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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