MétaCan
Menu
Back to cohort
Record W4293428611 · doi:10.1016/j.jacadv.2022.100065

Oxygen Supplementation and Hyperoxia in Critically Ill Cardiac Patients

2022· article· en· W4293428611 on OpenAlexaff
Alexander Thomas, Sean van Diepen, Rachel Beekman, Shashank S. Sinha, Samuel B. Brusca, Carlos L. Alviar, Jacob C. Jentzer, Erin A. Bohula, Jason N. Katz, Andi Shahu, Christopher F. Barnett, David A. Morrow, Emily J. Gilmore, Michael A. Solomon, P. Elliott Miller

Bibliographic record

VenueJACC Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Alberta
FundersNational Center for Advancing Translational SciencesNIH Clinical CenterNational Institutes of HealthAbbVie
KeywordsHyperoxiaMedicineIntensive care medicineHypoxic pulmonary vasoconstrictionHeart failureHypoxia (environmental)Intensive care unitMyocardial infarctionOxygen toxicityCardiologyPulmonary hypertensionShock (circulatory)Adverse effectCardiac outputInternal medicineHemodynamicsOxygenLung

Abstract

fetched live from OpenAlex

Oxygen supplementation has been a mainstay in the management of patients with acute cardiac disease. While hypoxia is known to be detrimental, the adverse effects of artificially high oxygen levels (hyperoxia) have only recently been recognized. Hyperoxia may induce harmful hemodynamic effects, including peripheral and coronary vasoconstriction, and direct cellular toxicity through the production of reactive oxygen species. In addition, emerging evidence has shown that hyperoxia is associated with adverse clinical outcomes. Thus, it is essential for the cardiac intensive care unit (CICU) clinician to understand the available evidence and titrate oxygen therapies to specific goals. This review summarizes the pathophysiology of oxygen within the cardiovascular system and the association between supplemental oxygen and hyperoxia in patients with common CICU diagnoses, including acute myocardial infarction, heart failure, shock, cardiac arrest, pulmonary hypertension, and respiratory failure. Finally, we highlight lessons learned from available trials, gaps in knowledge, and future directions.

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)0.003
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.278
Teacher spread0.271 · 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

Citations36
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJACC AdvancesSame topicCardiac Arrest and ResuscitationFrench-language works237,207