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Record W4416665365 · doi:10.3390/jcm14238352

Mulligan Mobilization Combined with Conventional Therapy vs. Conventional Care Alone in Patients with Rotator Cuff Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

2025· review· en· W4416665365 on OpenAlexaff
Abdullah Alqallaf, Abdullah M Alharran, Пламен Пенчев, Yousef Y. Alkandari, Bassam Almulla, Ahmed Al-Mulla, Abdullah AL-Shatti, Abdulrahman E. Alayyaf, Ahmad Alahmad, Abdulrahman Al-Naseem

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMulliganRotator cuffRange of motionRandomized controlled trialManual therapyQuality of life (healthcare)Cochrane LibraryJoint mobilization

Abstract

fetched live from OpenAlex

Background/Objectives: Rotator cuff disease (RCD) is one of the most common causes of shoulder dysfunction, often resulting in pain, limited range of motion (ROM), and reduced function. Mulligan Mobilization with Movement (MWM) has been proposed as an effective adjunct to conventional therapy by correcting positional faults and improving joint mechanics. However, the overall evidence in RCD remains inconclusive. This meta-analysis aimed to evaluate the efficacy of Mulligan mobilization combined with conventional therapy versus conventional therapy alone on pain, functionality, ROM, joint position sense, and quality of life (QoL) in patients with RCD. Methods: A comprehensive literature search was carried out in PubMed, Web of Science, Scopus, and the Cochrane Library from database inception to 12 October 2025, with no restrictions on publication year. We included randomized controlled trials (RCTs) that compared Mulligan mobilization combined with conventional therapy against conventional therapy alone in individuals with rotator cuff-related pain. The predefined outcomes were pain intensity, range of motion (ROM), quality of life (QoL), joint position sense, and functional performance. All statistical analyses were conducted using R version 4.3.1. Heterogeneity was assessed using the I2 statistic and the Cochrane Q test. Pooled mean differences (MDs) were calculated using the Inverse Variance approach with a restricted maximum-likelihood (REML) random-effects model. The review protocol was prospectively registered in PROSPERO (ID: CRD420251166854). Results: Four RCTs met the eligibility criteria and were included in the meta-analysis, comprising a total of 160 participants. Of these, 80 (50%) received Mulligan mobilization in combination with conventional therapy (mean age: 51 years; mean proportion of females: 45%). In the pooled analysis, Mulligan mobilization significantly improved pain at rest (MD −1.19; 95% CI [−1.64; −0.74]; p = 0.01; I2 = 0%), pain during activity (MD −2.25; 95% CI [−3.18; −1.31]; p = 0.01; I2 = 67%), functionality (MD −14.71; 95% CI [−20.10; −9.33]; p = 0.01; I2 = 51%), ROM (MD 19.92; 95% CI [11.25; 28.39]; p = 0.01; I2 = 58%), and joint position sense (MD −3.31; 95% CI [−6.22; −0.40]; p = 0.03; I2 = 80%) compared with conventional therapy alone. No significant difference was observed for QoL (MD 10.58; 95% CI [−3.18; 24.34]; p = 0.13; I2 = 76%). Conclusions: Mulligan mobilization combined with conventional therapy provides significant improvements in pain, functionality, ROM, and joint position sense in RCD. However, no statistically significant differences were observed in QoL between the groups. Integration of this technique into rehabilitation protocols may enhance clinical outcomes and functional recovery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0260.037
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.115
GPT teacher head0.469
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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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