Evidence mapPaperPMID 41863013Full record

ArticleSystematic reviews2026

What can we learn from network meta-analyses published in top medical journals.

Chen Tian, Mengting Li, Jingwen Jiang, Liangying Hou, Bei Pan, Yong Wang, Long Ge, Xiong Yue

Abstract read
In one paragraph

Article in Systematic reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Chen Tian *Department of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou, China.
Mengting Li *Department of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou, China.
Jingwen JiangThe 83rd Group Army Hospital of the Chinese People's Liberation Army, Xinxiang, China.
Liangying HouKey Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou University, Lanzhou, China.
Bei PanKey Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou University, Lanzhou, China.
Yong WangThe First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Long GeDepartment of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou, China. gelong2009@163.com.ORCID 0000-0002-3555-1107
Xiong YueGansu Provincial Maternal and Child-Care Hospital (Gansu Provincial Central Hospital), Lanzhou, China. 596703494@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionNetwork meta-analysis (NMA) is a key tool for comparing multiple treatments. However, its reliability can be compromised by inconsistencies in methodology and reporting. We conducted this meta-research study to assess the characteristics and quality of NMAs published between 2019 and 2023 in BMJ, Lancet, JAMA, NEJM, and Annals of Internal Medicine, to identify limitations and propose improvement strategies.

methodsWe sampled and evaluated NMAs from the target journals using a 36-item checklist. Reviewers independently extracted data and assessed methodological and reporting quality. We compared our sample's compliance with NMAs from specific domains (Anesthesia, Cancer, Acupuncture, and Cochrane reviews) using odds ratios (ORs) with 95% confidence intervals (CIs) to identify field-specific variations.

resultsOf the 43 included NMAs, the majority originated from Canada (n = 12, 27.91%), China (n = 9, 20.93%), and the UK (n = 9, 20.93%), with 70% published in The BMJ. Overall compliance was high (> 95%) for conducting risk-of-bias assessments, providing a full search strategy, and evaluating inconsistency. However, several key methodological aspects showed suboptimal adherence: statistician involvement (55.81%), transitivity assessment (53.49%), performance of subgroup analyses (53.49%), and meta-regression (44.19%) all had compliance below 60%. Stratified analyses indicated that both the involvement of statistician involvement and the type of intervention significantly influenced methodological and reporting quality. Comparative analyses quantified differences: top medical journals' NMAs were more likely to employ random-effects models than Anesthesia NMAs [OR = 5.49, 95%CI (1.90-15.84)], Cancer NMAs [OR = 10.86, 95%CI (3.95-29.88)], and Cochrane NMAs [OR = 12.35, 95%CI (4.02-37.90)]. They were also more likely to assess consistency compared with Anesthesia NMAs [OR = 47.79, 95%CI (6.18-369.38)], Cancer NMAs [OR = 144.26, 95%CI (18.82-1105.91)], and Cochrane NMAs [OR = 84.00, 95%CI (10.45-675.31)]. Compared with Anesthesia, Acupuncture, and Cochrane NMAs, top medical journal NMAs demonstrated superior methodological rigor and reporting quality, particularly in items related to data collection, presentation of network geometry, and reporting of additional analyses.

conclusionsFuture NMAs should incorporate rational living-update mechanisms and statistical expertise to ensure methodological rigor. Transitivity must be evaluated to confirm study homogeneity, while sensitivity analyses, subgroup analyses, and meta-regression should be systematically conducted to explore heterogeneity. Certainty-of-evidence assessments using standardized frameworks are essential to enhance transparency and to guide evidence-based clinical decision-making and guideline development.

Indexed as

Network Meta-Analysis as TopicPeriodicals as TopicHumansReproducibility of ResultsResearch DesignAMSTAR-2Meta-researchNetwork meta-analysisPRISMA-NMATop medical journal

Identifiers

PMID41863013
PMCPMC13127007

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.