Evidence map›Paper›PMID 39956636›Full record

ReviewKorean journal of anesthesiology2025

Heterogeneity in meta-analyses: an unavoidable challenge worth exploring.

Geun Joo Choi, Hyun Kang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Korean journal of anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 34 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 34 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

71 citing papers in PubMed, 34 syntheses or guidelines pooled it.

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  2. Red cell distribution width as a prognostic predictor for colorectal cancer: a meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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11 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Geun Joo ChoiDepartment of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea.
Hyun KangDepartment of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneity is a critical but unavoidable aspect of meta-analyses that reflects differences in study outcomes beyond what is expected by chance. These variations arise from differences in the study populations, interventions, methodologies, and measurement tools and can influence key meta-analytical outputs, including pooled effect sizes, confidence intervals, and overall conclusions. Systematic reviews and meta-analyses combine evidence from diverse studies; thus, a clear understanding of heterogeneity is necessary for reliable and meaningful interpretations of the results. This review examines the concepts, sources, measurement techniques, and implications of this heterogeneity. Statistical tools (e.g., Cochran's Q, I2, and τ2) quantify heterogeneity, whereas τ and prediction intervals, as they use the same units, aid in the intuitive understanding of heterogeneity. The choice between fixed- and random-effects models can also significantly affect the handling and interpretation of heterogeneity in meta-analyses. Effective management strategies include subgroup analyses, sensitivity analyses, and meta-regressions, which identify sources of variability and strengthen the robustness of the findings. Although heterogeneity complicates the synthesis of a single effect size, it offers valuable insights into patterns and differences among studies. Recognizing and understanding heterogeneity is vital for accurately synthesizing the evidence, which can indicate whether an intervention has consistent effects, benefits, or harms. Rather than viewing heterogeneity as inherently good or bad, researchers and clinicians should consider it a key component of systematic reviews and meta-analyses, allowing for a deeper understanding and more nuanced application of pooled findings. Addressing heterogeneity ultimately enhances the reliability, applicability, and overall impact of the conclusions of meta-analyses.

Indexed as

Meta-Analysis as TopicData Interpretation, StatisticalHumansBiasBiostatisticsEpidemiologyEvidence-based medicineHeterogeneityMeta-analysis as topicSystematic Review.

Identifiers

PMID39956636
PMCPMC12326561

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.