Evidence map›Paper›PMID 41042338›Full record

ArticleJournal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology2025

Active and passive physical therapy in patients with chronic low-back pain: a level I Bayesian network meta-analysis.

Alice Baroncini, Nicola Maffulli, Nicola Manocchio, Michela Bossa, Calogero Foti, Luise Schäfer, Alexandra Klimuch, Filippo Migliorini

Abstract readNetwork Meta-Analysis
In one paragraph

Article in Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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.

Alice BaronciniDepartment of Spine Surgery, Casa di Cura Humanitas San Pio X, Milan, Italy.
Nicola MaffulliFaculty of Medicine and Psychology, University "La Sapienza" of Rome, Rome, Italy.
Nicola ManocchioPhysical and Rehabilitation Medicine, Clinical Sciences and Translational Medicine Department, Tor Vergata University, Rome, Italy.
Michela BossaPhysical and Rehabilitation Medicine, Clinical Sciences and Translational Medicine Department, Tor Vergata University, Rome, Italy.
Calogero FotiPhysical and Rehabilitation Medicine, Clinical Sciences and Translational Medicine Department, Tor Vergata University, Rome, Italy.
Luise SchäferDepartment of Orthopaedic and Trauma Surgery, Eifelklinik St. Brigida, Kammerbruschstr. 8, Simmerath, 52152, Aachen, Germany.
Alexandra KlimuchDepartment of Spine Surgery, Casa di Cura Humanitas San Pio X, Milan, Italy.
Filippo MiglioriniDepartment of Trauma and Reconstructive Surgery, University Hospital of Halle, Martin-Luther University Halle-Wittenberg, Ernst-Grube-Street 40, 06097, Halle (Saale), Germany. filippo.migliorini@uk-halle.de.ORCID http://orcid.org/0000-0001-7220-1221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic low back pain (cLBP) is common. Physiotherapy is frequently indicated as a non-pharmacological management of these patients. This Bayesian network meta-analysis compared active versus passive physiotherapy versus their combination in terms of pain and disability in patients with mechanical and/or aspecific cLBP.

methodsIn June 2025, the following databases were accessed: PubMed, Web of Science, Google Scholar and Embase. All the randomised controlled trials (RCTs) which evaluated the efficacy of a physiotherapy program in patients with LBP were accessed. Data regarding pain scores, the Roland-Morris Disability Questionnaire (RMQ) and the Oswestry Disability Index (ODI) were collected. The network meta-analyses were performed using the STATA (version 14; StataCorp, College Station, TX, USA) routine for Bayesian hierarchical random-effects model analysis, employing the inverse variance method. The standardised mean difference (STD) was used for continuous data.

resultsData from 2768 patients (mean age 46.9 ± 10.9 years, mean BMI 25.8 ± 2.9 kg/m

conclusionActive physiotherapy showed better results than passive physiotherapy and a combination of both for the management of mechanical and/or non-specific cLBP. LEVEL OF EVIDENCE: Level I, Bayesian network meta-analysis of RCTs.

Indexed as

Chronic PainLow Back PainPhysical Therapy ModalitiesBayes TheoremHumansMiddle AgedPain MeasurementRandomized Controlled Trials as TopicLow back painPhysiotherapySpine

Identifiers

PMID41042338
PMCPMC12494532

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.