Evidence map›Paper›PMID 42755726›Full record

SynthesisFrontiers in physiology2026

Prevalence of rotator cuff tears: a systematic review and meta-analysis of observational studies.

Yaocheng Yang, Xinlei Fu, Shilong Meng, Musen Zhong

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in physiology, 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

4 authors.

Yaocheng Yang *Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China.
Xinlei Fu *The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Shilong Meng *The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Musen ZhongChunan County Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rotator cuff tears are a major cause of shoulder pain, functional impairment, and reduced quality of life. However, reported prevalence estimates vary substantially across population settings, tear definitions, and diagnostic methods. This study aimed to quantify the prevalence of rotator cuff tears and identify factors contributing to variation across studies. Methods: We conducted a systematic review and meta-analysis of observational studies reporting the prevalence of rotator cuff tears. CNKI, Wanfang, VIP, PubMed, Web of Science, Embase, and the Cochrane Library were searched from database inception to December 1, 2025. Studies based on ultrasound or magnetic resonance imaging (MRI) and reporting prevalence on a per-person basis were included. Two reviewers independently performed study screening, data extraction, and quality assessment using the Agency for Healthcare Research and Quality (AHRQ) cross-sectional study appraisal tool. Statistical analyses were performed using Stata 18.0 and Python 3.13. Results: Seventeen studies representing 19 independent datasets and 5,988 participants from eight countries were included. The pooled prevalence was 38.6% Conclusion: Rotator cuff tears are common, particularly among symptomatic and high-exposure populations, underscoring the need for targeted prevention, risk assessment, and early detection. The marked variation across population settings further highlights the importance of population-specific prevalence estimates for clinical and epidemiological planning.

Indexed as

epidemiologymeta-analysisprevalencerotator cuff tearsystematic review

Identifiers

PMID42755726
PMCPMC13581783

What OpenQuestion holds

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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 OpenQuestion graph.