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Record W2079444133 · doi:10.1097/ccm.0000000000000824

Chest Compression Rates and Survival Following Out-of-Hospital Cardiac Arrest*

2015· article· en· W2079444133 on OpenAlexafffund
Ahamed H. Idris, Danielle Guffey, Paul E. Pepe, Siobhan P. Brown, Steven C. Brooks, Clifton W. Callaway, Jim Christenson, Daniel P. Davis, Mohamud Daya, Randal Gray, Peter J. Kudenchuk, Jonathan Larsen, Steve Lin, James J. Menegazzi, Kellie Sheehan, George Sopko, Ian G. Stiell, Graham Nichol, Tom P. Aufderheide

Bibliographic record

VenueCritical Care Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of OttawaUniversity of TorontoOttawa HospitalUniversity of British ColumbiaQueen's University
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchEuropean Resuscitation Council
KeywordsMedicineCardiopulmonary resuscitationReturn of spontaneous circulationData compression ratioResuscitationSurvival rateCompression (physics)Logistic regressionCardiologyInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Guidelines for cardiopulmonary resuscitation recommend a chest compression rate of at least 100 compressions/min. A recent clinical study reported optimal return of spontaneous circulation with rates between 100 and 120/min during cardiopulmonary resuscitation for out-of-hospital cardiac arrest. However, the relationship between compression rate and survival is still undetermined. DESIGN: Prospective, observational study. SETTING: Data is from the Resuscitation Outcomes Consortium Prehospital Resuscitation IMpedance threshold device and Early versus Delayed analysis clinical trial. PARTICIPANTS: Adults with out-of-hospital cardiac arrest treated by emergency medical service providers. INTERVENTIONS: None. MEASUREMENTS MAIN RESULTS: Data were abstracted from monitor-defibrillator recordings for the first five minutes of emergency medical service cardiopulmonary resuscitation. Multiple logistic regression assessed odds ratio for survival by compression rate categories (<80, 80-99, 100-119, 120-139, ≥140), both unadjusted and adjusted for sex, age, witnessed status, attempted bystander cardiopulmonary resuscitation, location of arrest, chest compression fraction and depth, first rhythm, and study site. Compression rate data were available for 10,371 patients; 6,399 also had chest compression fraction and depth data. Age (mean±SD) was 67±16 years. Chest compression rate was 111±19 per minute, compression fraction was 0.70±0.17, and compression depth was 42±12 mm. Circulation was restored in 34%; 9% survived to hospital discharge. After adjustment for covariates without chest compression depth and fraction (n=10,371), a global test found no significant relationship between compression rate and survival (p=0.19). However, after adjustment for covariates including chest compression depth and fraction (n=6,399), the global test found a significant relationship between compression rate and survival (p=0.02), with the reference group (100-119 compressions/min) having the greatest likelihood for survival. CONCLUSIONS: After adjustment for chest compression fraction and depth, compression rates between 100 and 120 per minute were associated with greatest survival to hospital discharge.

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)0.006
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.347
Teacher spread0.316 · 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

Citations345
Published2015
Admission routes2
Has abstractyes

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