Evidence map›Paper›PMID 39981187›Full record

SynthesisFrontiers in pharmacology2025

Efficacy and safety of Xiaoyao San in the treatment of chronic fatigue syndrome: a systematic review and meta-analysis.

Qianqian Wang, Jian Zhou, Guanwen Gong

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Efficacy and safety of modifiedFrontiers in pharmacology · 2026
    Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Qianqian WangDepartment of Preventive Treatment of Diseases, Nantong Hospital of Traditional Chinese Medicine, Nantong, China.
Jian ZhouDepartment of Preventive Treatment of Diseases, Nantong Hospital of Traditional Chinese Medicine, Nantong, China.
Guanwen GongDepartment of General Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Xiaoyao San (XYS) has been increasingly used in China for treating chronic fatigue syndrome (CFS), but its efficacy and safety remain unclear. Objective: To systematically evaluate the efficacy and safety of XYS compared to standard biomedical treatments (SBT) in CFS patients. Methods: A comprehensive search of English and Chinese databases was conducted up to December 2024. Eligible studies included randomized controlled trials comparing XYS or XYS + SBT to SBT alone. Primary outcomes were effective rate (ER) and fatigue scale-14 (FS-14). Secondary outcomes included self-rating anxiety scale (SAS), self-rating depression scale (SDS), and adverse events (AEs). Data were analyzed using Review Manager 5.4, and evidence quality was assessed using the GRADE approach. Results: Six studies involving 623 patients were included. The meta-analysis showed that XYS-based interventions significantly improved ER (RR = 1.27, 95% CI: 1.18-1.37, I Conclusion: XYS, whether alone or with SBT, is effective and safe for improving ER, fatigue, anxiety, and depression in CFS patients. However, due to the low quality of the evidence, results should be interpreted cautiously. High-quality RCTs with larger sample sizes and longer follow-up are needed to provide stronger evidence for the clinical use of XYS in managing CFS. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=493084, identifier CRD42023493084.

Indexed as

chronic fatigue syndromemeta-analysisstandard biomedical treatmentssystematic reviewtraditional Chinese medicineXiaoyao San

Identifiers

PMID39981187
PMCPMC11839657

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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 OpenQuestion graph.