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Record W4417152880 · doi:10.31083/rcm43774

Predictive Value of Red Blood Cell Distribution Width for the Prognosis of Cardiac Arrest: A Systematic Review and Meta-Analysis

2025· article· en· W4417152880 on OpenAlexaboutno aff
Yufeng Zhang, B Zhang, Maoxian Yang

Bibliographic record

VenueReviews in Cardiovascular Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsRed blood cell distribution widthPredictive valueDistribution (mathematics)Predictive value of testsValue (mathematics)

Abstract

fetched live from OpenAlex

Background: While a potential relationship between red cell distribution width (RDW) and cardiac arrest (CA) prognosis has been raised, the question of whether there is robust data to support this connection remains open. To examine the association of red blood cell distribution width with prognosis in patients with cardiac arrest. Methods: This study strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant studies were identified from searches conducted in the PubMed, Web of Science, the Cochrane Library, and Embase electronic databases up to March 12, 2025. The Newcastle-Ottawa scale was used to assess the quality of the included studies. The combined effect size was determined utilizing standardized mean differences (SMDs), hazard ratios (HRs), and 95% confidence intervals (CIs). Subgroup analyses were also performed to elucidate the sources of heterogeneity. Simultaneously, we also pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratios (ORs), and the area under the summary receiver operating characteristic curve (AUROC). Results: This meta-analysis encompassed eight studies involving CA patients with CA. Our results suggested that patients who died after CA exhibited higher RDW levels than those who survived (SMD = 0.45; 95% CI: 0.30-0.60). There was a 1.63-fold higher risk of death in CA patients with high RDW levels versus those with low levels (95% CI: 1.27-2.08). The SEN, SPE, and AUC for using the RDW to predict mortality were 0.82 (95% CI: 0.74-0.88), 0.49 (95% CI: 0.23-0.74), and 0.80 (95% CI: 0.76-0.83), respectively. Conclusions: RDW is a relatively accurate predictor of prognosis in patients after CA. Thus, using RDW can provide some insights into patient outcomes, enabling healthcare professionals to make informed decisions in advance. The PROSPERO registration: CRD420251023018, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251023018.

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)0.010
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.591
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0000.002
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 designMeta-analysis
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

Citations0
Published2025
Admission routes1
Has abstractyes

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