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Record W4416723817 · doi:10.1186/s12865-025-00783-8

Prognostic value of monocyte chemoattractant protein-1 in sepsis: a systematic review and meta-analysis

2025· article· en· W4416723817 on OpenAlexaboutno aff
Zhonghui Huang, Nannan Li, Wei Wang, Qing Wang, Wen Ye

Bibliographic record

VenueBMC Immunology · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)SepsisMonocyteChemotaxisInflammationCytokine

Abstract

fetched live from OpenAlex

BACKGROUND: Monocyte chemoattractant protein-1 (MCP-1) has been utilized as a prognostic indicator for sepsis patients; however, the findings have been inconsistent. This meta-analysis seeks to elucidate the prognostic significance of MCP-1 in individuals diagnosed with sepsis. METHODS: We systematically queried four databases (Web of Science, PubMed, EMBASE, Cochrane Library) to identify studies published from the establishment of the databases until January 2025. Hazard ratios (HRs) with 95% CIs were employed to evaluate the prognostic significance of MCP-1 in this patient population. In our study, the Newcastle-Ottawa Quality Assessment Scale was evaluated for quality assessment, and Funnel plot was used for publication offset. RESULTS: A total of 592 patients from eight studies were included in the evaluation. The findings revealed that MCP-1 levels were significantly elevated in non-survivors compared to those who survived [SMD = 0.54, 95% CI: 0.25-0.82, P = 0.0002]. Our study demonstrated that elevated levels of MCP-1 were significantly associated with an unfavorable prognosis in patients with sepsis, with a hazard ratio (HR) of 1.36 (95% confidence interval: 1.25-1.48; P < 0.00001) using a random-effects model. The funnel plot indicated no publication bias. CONCLUSION: This meta-analysis suggests that elevated levels of MCP-1 could serve as a valuable prognostic indicator in sepsis patients, however, this conclusion still requires validation through higher-quality studies with longer-term follow-up.

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)0.001
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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 head0.090
GPT teacher head0.357
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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