Evidence map›Paper›PMID 42215508›Full record

ArticleScientific reports2026

The role of biopsychosocial factors in classifying pain intensity across various chronic pain conditions.

Iara De Schoenmacker, Maria Monzon, Laura Sirucek, Paulina S Scheuren, Robin Lütolf, Lindsay M Gorrell, Florian Brunner, Armin Curt, Jan Rosner, Petra Schweinhardt and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Iara De SchoenmackerBiomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. deschoeia@gmail.com.
Maria MonzonBiomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland.
Laura SirucekNeuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
Paulina S ScheurenSpinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Robin LütolfSpinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Lindsay M GorrellDepartment of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Florian BrunnerPhysical Medicine and Rheumatology, Balgrist University Hospital, Zurich, Switzerland.
Armin CurtSpinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Jan RosnerSpinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Petra SchweinhardtDepartment of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Michèle HubliSpinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Catherine R JutzelerBiomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland.

Funding

International Foundation for Research in Paraplegia PF198FLundbeck Foundation R359-2020-2620Michael Smith Health Research BC RT-2023-3173Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung P500PB_214416Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung PZ00P3_186101Strategic Focus Area "Personalized Health and Related Technologies (PHRT)" of the Swiss Federal Institutes of Technology 380
6 · The paper itself

Abstract

Chronic pain is a multifaceted condition shaped by biological, psychological, and social factors, which challenges its diagnosis and treatment. While prior research largely focused on sensory profiles to distinguish pain mechanisms, this study adopted a biopsychosocial perspective with the key addition of experimental pain paradigms to identify factors explaining variance in chronic pain severity across diverse etiologies. The dataset comprised 101 individuals with chronic pain and 63 pain-free controls. Participants were classified into three groups (no pain, mild-to-moderate pain, and severe pain) using three models: a sensory-profile-based model including quantitative sensory testing (QST model), a biopsychosocial model incorporating QST alongside psychological and social variables (biopsychosocial model), and a model using the same biopsychosocial data but excluding QST measures (noQST model). The QST model achieved an accuracy of 0.51 and an F1-score of 0.48. In contrast, the biopsychosocial model performed best, with both accuracy and F1-score of 0.71, while the noQST model reached 0.60 for both metrics. Key predictors in the best-performing model included quality of life, loss of sensation, depressive mood, pain catastrophizing, anxiety, fatigue, and general health. Overall, biopsychosocial factors enhanced chronic pain severity classification beyond sensory profiles alone across heterogeneous pain etiologies.

Indexed as

Chronic PainModels, BiopsychosocialPain MeasurementAdultFemaleHumansMaleMiddle AgedSeverity of Illness IndexBiopsychosocial modelChronic painMachine learningPain classificationQuantitative sensory testing

Identifiers

PMID42215508
PMCPMC13457856

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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.