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Record W4415223698 · doi:10.1080/10903127.2025.2575439

Effectiveness of Sodium Bicarbonate Administration in Out-of-Hospital Cardiac Arrests: An Updated Systematic Review and Meta-Analysis

2025· article· en· W4415223698 on OpenAlexaff
Guilherme Pozueco Zaffari, Ivo Queiroz, Julio Soares Curi, Gabriel Lobo

Bibliographic record

VenuePrehospital Emergency Care · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSt. Lawrence CollegeKingston Health Sciences Centre
Fundersnot available
KeywordsSodium bicarbonateHyperkalemiaTricyclic antidepressantAdministration (probate law)ResuscitationSodiumAirway

Abstract

fetched live from OpenAlex

OBJECTIVES: The use of sodium bicarbonate (SB) in the treatment of cardiac arrest without known etiology is still controversial and is not routinely recommended by the American Heart Association guidelines. This study summarizes current evidence on the use of SB in out-of-hospital cardiac arrest (OHCA). METHODS: We systematically searched PubMed, Scopus, and Cochrane databases for randomized controlled trials (RCTs) and observational studies evaluating SB in OHCA. Return of spontaneous circulation (ROSC) was the primary outcome, explicitly defined as the return of a palpable pulse and measurable blood pressure; sustained ROSC (≥20 min) was noted separately. Survival to discharge was a secondary outcome. Using a random-effects model, we pooled Odds Ratios (OR) for binary outcomes with 95% confidence intervals (CI). RESULTS: = 0.48). CONCLUSIONS: Administration of sodium bicarbonate in patients with OHCA was not associated with improved ROSC or survival to discharge. Therefore, its routine use cannot be recommended. However, our review does not exclude the possibility of benefit in selected, guideline-specified contexts such as hyperkalemia or tricyclic antidepressant overdose, or when administered early during resuscitation. Further high-quality studies with standardized reporting of etiology, timing, dose, and airway management are needed to clarify these potential indications.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.012
GPT teacher head0.313
Teacher spread0.300 · 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 designMeta-analysis
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

Citations1
Published2025
Admission routes1
Has abstractyes

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