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Record W7135072640 · doi:10.4187/respcare.07520443

Should All Patients Be Treated With Hypothermia Following Cardiac Arrest?

2007· article· en· W7135072640 on OpenAlexaboutno aff
Steven Deem, William E. Hurford

Bibliographic record

VenueRespiratory Care · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsHypothermiaClinical deathReturn of spontaneous circulationRandomized controlled trialTargeted temperature managementTherapeutic effectResuscitationTherapeutic approach

Abstract

fetched live from OpenAlex

Cardiac arrest is a common and lethal medical problem; each year more than half a million people in the United States and Canada suffer cardiac arrest treated by emergency medical personnel or in-hospital providers. Of those who survive to hospital admission or suffer in-hospital arrest, 40–60% die prior to discharge. Neurologic injury is the major source of morbidity and mortality after recovery of spontaneous circulation. Therapeutic options to prevent neurologic injury are limited, but recent randomized trials showed that moderate therapeutic hypothermia improves neurologic outcome in selected patients following cardiac arrest. Clear consensus statements recommend that unconscious adult patients with spontaneous circulation after out-of-hospital cardiac arrest should be cooled if the initial rhythm was ventricular fibrillation, and that therapeutic hypothermia should be considered for other patients (other rhythms or in-hospital arrest). However, the position that all patients should be cooled following cardiac arrest is probably too broad, given the lack of studies on patients with non-ventricular-fibrillation rhythms, in-hospital arrest, or non-cardiac causes of arrest. Further research is needed to determine the broadest application of moderate therapeutic hypothermia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.292
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 teacher head, 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
Published2007
Admission routes1
Has abstractyes

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