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Oxygen and carbon dioxide targets after cardiac arrest: an updated systematic review

2025· review· en· W4409727615 on OpenAlexaff
Mathias J. Holmberg, Takanari Ikeyama, Rakesh Garg, Ian R. Drennan, Eric J. Lavonas, Janet Bray, Theresa M. Olasveengen, Katherine M. Berg

Bibliographic record

VenueResuscitation · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSunnybrook Health Science Centre
FundersDeakin University
KeywordsMedicineCarbon dioxideOxygenIntensive care medicine

Abstract

fetched live from OpenAlex

AIM: To perform an updated systematic review and meta-analysis of oxygen and carbon dioxide targets in patients with sustained return of spontaneous circulation after cardiac arrest. METHODS: Searches were conducted in MEDLINE, Embase, and Evidence-Based Medicine Reviews from August 2019 to March 2025 for randomised trials comparing specific oxygen or carbon dioxide targets in post-cardiac arrest patients. Two investigators independently reviewed trials for relevance, extracted data, and assessed risk of bias. Data were pooled using random-effects models. The certainty of evidence was evaluated using GRADE methodology. RESULTS: Fifteen manuscripts from 12 trials were included. All trials were limited to adult patients, primarily including out-of-hospital cardiac arrests. Five trials evaluated oxygen targets in the prehospital setting, while six evaluated oxygen targets and three evaluated carbon dioxide targets in the intensive care unit setting. Risk of bias was assessed as moderate for most outcomes. Meta-analyses found no differences in survival or favourable functional outcomes when comparing restrictive to liberal oxygen targets in either setting. There was also no difference in outcomes when comparing mild hypercapnia to normocapnia. The certainty of evidence was rated as low to moderate. CONCLUSIONS: Among patients resuscitated from cardiac arrest, neither restrictive oxygen targets nor mild hypercapnia, compared to conventional targets, improved survival or functional outcomes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.306
Teacher spread0.293 · 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 designSystematic review
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

Citations8
Published2025
Admission routes1
Has abstractyes

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