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Association of Mechanical Cardiopulmonary Resuscitation Device Use With Cardiac Arrest Outcomes

2016· letter· en· W2565529313 on OpenAlexaff
David G. Buckler, Rita V. Burke, Maryam Y. Naim, Andrew MacPherson, Richard N Bradley, Benjamin S. Abella, Joseph W. Rossano

Bibliographic record

VenueCirculation · 2016
Typeletter
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationEmergency departmentResuscitationPopulationEmergency medicineMedical emergencyPsychiatry

Abstract

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he use of mechanical cardiopulmonary resuscitation devices (mCPR) to deliver CPR has become more widespread, although a survival advantage has not been demonstrated in randomized, controlled trials.[1][2][3] Little is known about real-world use of mCPR or the association with outcomes.CARES (Cardiac Arrest Registry to Enhance Survival), a US national registry of outof-hospital cardiac arrest, 4 was analyzed for adults with nontraumatic out-of-hospital cardiac arrest from January 2013 to December 2015.Patients treated with mCPR were compared with patients receiving manual CPR only.Time of arrest, time of first CPR, and timing of interventions were not reliably reported.However, patients had information about when return of spontaneous circulation (ROSC) occurred before or after advanced life support (ALS) measures.As part of a subgroup analysis, patients with ROSC before ALS were excluded because of the decreased likelihood of these patients receiving mCPR.The primary outcome of interest was neurologically favorable survival at hospital discharge, defined as a Cerebral Performance Category of 1 or 2. This project was deemed exempt from review by the Children's Hospital of Philadelphia and University of Pennsylvania Institutional Review boards.Statistical analyses included the Student t test and χ 2 test as appropriate.A multivariable logistic regression model was created with the use of stepwise addition to control for Utstein-style arrest characteristics, including age, arrest location, bystander CPR and automated external defibrillator use, witnessed arrest status, initial rhythm, postarrest targeted temperature management, successful advanced airway placement, and impedance threshold device use.Separate analyses were performed for the cohort who did not have ROSC before ALS treatment.Statistical significance was defined as a 2-sided value of P<0.05, and analyses were performed with SAS/STAT version 9.4 (SAS Institute Inc, Cary, NC).During the study period, 80 861 subjects were included in the analysis (Figure ).The median age was 62 years (interquartile range, 52-75 years), and 35.1% received bystander CPR.Compared with patients receiving manual CPR, those receiving mCPR were more likely to have an unwitnessed arrest (57.3% versus 55.7%), an automated external defibrillator placed (33.3% versus 28.3%), an advanced airway placed (87.4% versus 79.0%), and an impedance threshold device used (41.8% versus 13.4%) and to undergo prehospital targeted temperature management (16.6%versus 12.2%; P<0.05 for all).From 2013 to 2015, use of mCPR increased from 20.6% to 23.4% (P<0.0001), and mCPR was used at least once by 41.9% (244 of 582) of emergency medical services agencies.For all agencies that used a mechanical device, median mCPR use was 43.9% (interquartile range, 11.9%-59.9%).However, agency use varied greatly, with 21.7% of agencies using mCPR in >75% of arrests and 37.7% using mCPR in <25% of arrests.Survival to hospital discharge and neurologically favorable survival were greater in patients not receiving mCPR (11.3% versus 7.0%, P<0.0001 for overall survival; 9.5% versus 5.6%, P<0.0001 for neurologically favorable survival).When patients

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)0.025
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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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Citations34
Published2016
Admission routes1
Has abstractyes

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