Evidence map›Paper›PMID 41853734›Full record

ArticleJSES reviews, reports, and techniques2026

Validation of a web-based questionnaire for shoulder disorder diagnosis and staging.

Hiroki Shimizu, Daiki Nohara, Momoko Nagai-Tanima, Tomoki Aoyama

Abstract read
In one paragraph

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

Hiroki ShimizuHuman Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Daiki NoharaHuman Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Momoko Nagai-TanimaHuman Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Tomoki AoyamaHuman Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Shoulder pain is one of the most common musculoskeletal disorders globally, often requiring prolonged care and resulting in high healthcare costs. Telemedicine and remote rehabilitation show promise in managing these conditions, but standardized and reliable assessment tools remain limited. This study aimed to validate a novel web-based, self-administered questionnaire for estimating disease phases and specific diagnoses of shoulder disorders. Methods: A cross-sectional study was conducted with 30 participants reporting shoulder pain or restricted shoulder movement. Each participant completed a web application consisting of 33 symptom- and movement-related questions. The application guided users through the assessment using written and video instructions. Two experienced physical therapists independently evaluated each participant. In cases of disagreement, they discussed until reaching a consensus. Agreement rates between the web application and the therapists' consensus diagnoses were calculated in 4 domains: (1) classifying cases as shoulder-related vs. non-shoulder-related conditions, (2) classifying disease phase (inflammatory vs. noninflammatory), (3) identifying specific disorders, and (4) agreement across all 3 criteria. Results: The application accurately distinguished shoulder disorders from other conditions in 100% of cases. Among 27 participants with shoulder disorders, the disease phase was correctly classified in 92.6%, and specific diagnoses in 74.1%. Overall agreement across all 3 domains was 70%. Rotator cuff injuries were the most common diagnosis (70%). Conclusion: The web application showed high diagnostic agreement with experienced physical therapists and may be a useful screening and diagnostic tool for shoulder disorders in both clinical and remote settings. Larger studies are needed to confirm its utility and improve the algorithm.

Indexed as

DiagnosisRemote rehabilitationScreeningScreening toolShoulder disordersTelemedicineWeb application

Identifiers

PMID41853734
PMCPMC12993132

What OpenQuestion holds

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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 OpenQuestion graph.