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Record W2987762960 · doi:10.1136/heartjnl-2019-315340

Cochrane corner: Are mechanical compressions better than manual compressions in cardiac arrest?

2019· review· en· W2987762960 on OpenAlexaff
Peter Wang, Steven C. Brooks

Bibliographic record

VenueHeart · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationResuscitationDecompressionDefibrillationCompression (physics)CardiologyAnesthesiaSurgery

Abstract

fetched live from OpenAlex

The quality of cardiopulmonary resuscitation (CPR) is associated with the probability of survival in people experiencing sudden cardiac arrest.1 Features of CPR quality associated with clinical outcomes include compression rate, compression depth and compression fraction (the proportion of time during a resuscitation attempt when chest compressions are being provided). Evidence suggests that chest compression quality can suffer because of suboptimal team leadership, provider fatigue or distraction with other resuscitation activities (eg, airway management, patient transfer, pulse checks).1 Mechanical chest compression devices have been developed to provide more consistent high quality chest compressions. Devices available on the market today use one of two fundamental mechanisms to facilitate chest compression. Some employ a piston mechanism positioned over the sternum. The LUCAS device, which employs a piston, also includes a suction cup interface with the chest wall to allow active decompression during the recoil phase of the duty cycle. Other devices, namely the ZOLL Autopulse, use a load-distributing band which encircles the chest of the patient. Activation of the device causes a rhythmic shortening and lengthening of the band to compress the chest circumferentially. Early evidence, including animal studies and observational studies in humans suggested that mechanical chest compression devices might be superior to conventional manual chest compressions during cardiac arrest.2 However, there has always been a concern that the devices may cause injury (eg, internal organ trauma) or may introduce dangerous interruptions in chest compressions while they are being deployed during resuscitation attempts. Several clinical trials have been published to explore the effectiveness of mechanical chest compression devices compared with standard manual compressions during cardiac arrest. Our Cochrane systematic review sought to evaluate these clinical trials and determine the relative effectiveness of these two strategies for cardiac arrest.2 We performed a systematic review by searching the following databases: …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.052
GPT teacher head0.381
Teacher spread0.329 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2019
Admission routes1
Has abstractyes

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