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Record W2292690659 · doi:10.1161/circ.128.suppl_22.a18

Abstract 18: Differential Effect of Analyze Early versus Analyze Late prior to Initial Rhythm Analysis in Out-of-Hospital Cardiac Arrest: the Role of EMS Agency

2013· article· en· W2292690659 on OpenAlexaff
Thomas D. Rea, David K. Prince, Laurie J. Morrison, Clifton W. Callaway, Tom P. Aufderheide, Mohamud Daya, Ian G. Stiell, Craig R. Warden, Lois Van Ottingham, Peter J. Kudenchuk, Myron L. Weisfeldt

Bibliographic record

VenueCirculation · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of British ColumbiaUniversity of OttawaSt. Michael's Hospital
Fundersnot available
KeywordsMedicineRhythmAgency (philosophy)Differential effectsCardiologyInternal medicineIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

Background: An effective resuscitation strategy in one community may not be effective in another community. We hypothesized that a strategy that prioritized initial Emergency Medical Services (EMS) rhythm analysis (Analyze Early) would provide a survival advantage among EMS systems with lower baseline ventricular fibrillation (VF) survival, while one prioritizing initial EMS CPR (Analyze Late) would provide a survival advantage among systems with higher baseline VF survival. Methods: We conducted an ancillary study of Analyze Early versus Analyze Late randomized trial of the Resuscitation Outcomes Consortium. Subjects were stratified according to pretrial EMS agency VF survival and their randomization arm. The primary outcome was survival to hospital discharge with favorable functional status. We used a binomial family mixed-effects model to determine whether outcomes from the trial intervention differed according to pretrial EMS agency VF survival. Results: Characteristics were similar among patients randomized to Analyze Early (n=4964) versus Analyze Late (n=4426). For EMS agencies with pretrial VF survival ≤20%, Analyze Early compared to Analyze Late was associated with a higher likelihood of favorable functional survival (5.5% versus 3.8%, OR=1.50 [1.11,2.0]). Conversely, among agencies with pretrial VF survival > 20%, Analyze Early compared to Analyze Late was associated with lower likelihood of favorable functional survival (6.1% versus 7.5%, OR=0.82 [0.66,1.02]). In the multivariable model, for every 10% improvement in pretrial VF survival, Analyze Late versus Analyze Early was associated with a 34% increase in the odds of favorable functional survival (OR=1.34 [1.07-1.66]). Conclusion: In this ancillary investigation of a clinical trial evaluating initial EMS CPR strategy, we observed a robust interaction indicating the Analyze Early strategy was associated with greater survival among EMS agencies with lower pretrial VF survival while the Analyze Late strategy was associated with greater survival among EMS agencies with a higher pretrial VF survival. The findings support distinct EMS CPR strategies based on a system’s baseline VF survival in order to achieve optimal outcomes following out-of-hospital cardiac arrest.

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)0.006
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.276
Teacher spread0.267 · 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
Published2013
Admission routes1
Has abstractyes

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