Evidence map›Paper›PMID 42762256›Full record

ArticleScandinavian journal of primary health care2026

Explaining symptoms in general practice: a predictive processing framework for primary care consultations.

Kees van Boven, Peter Lucassen, Tim Olde Hartman, Omer Van den Bergh

Abstract read
In one paragraph

Article in Scandinavian journal of primary health care, 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.

Kees van BovenDepartment of Primary and Community Care Radboudumc, Nijmegen, Gelderland, The Netherlands.ORCID 0000-0002-4422-9443
Peter LucassenDepartment of Primary and Community Care Radboudumc, Nijmegen, Gelderland, The Netherlands.ORCID 0000-0003-2955-2119
Tim Olde HartmanDepartment of Primary and Community Care Radboudumc, Nijmegen, Gelderland, The Netherlands.ORCID 0000-0003-2078-1206
Omer Van den BerghHealth Psychology, University of Leuven, KU Leuven, Belgium.ORCID 0000-0001-6394-7363

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo present predictive processing as a clinically useful framework for explaining symptom variability in general practice consultations.

methodsDevelopment of a conceptual framework through an iterative process of theoretical synthesis and clinical translation, informed by a supporting literature search integrating literature on predictive processing and symptom perception with primary care literature on consultation practice and persistent physical symptoms.

resultsPredictive processing conceptualises symptom perception as an interaction between bodily signals and the brain's prior expectations. Symptoms remain real and potentially disabling even when pathology is stable or minimal. The framework provides a structured approach for consultations: validating patient experiences, explaining symptom persistence, and guiding recovery-oriented management. A worked consultation example illustrates clinical application.

conclusionsPredictive processing offers a coherent framework to help general practitioners explain symptoms in primary care. Its limitations, including restricted prescriptive guidance and limited direct trial evidence, are acknowledged. The framework supports patient understanding and guides management focused on gradual recovery.

Indexed as

General PracticePhysician-Patient RelationsPrimary Health CareReferral and ConsultationHumansbiopsychosocial modelconsultationgeneral practicepersistent physical symptomsPredictive processingprimary caresymptom explanation

Identifiers

PMID42762256
PMCPMC13591960

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.