Beyond diagnosis in general practice: Predictive processing, a novel framework for persistent physical symptoms 2026 van Boven et al

Andy

Andy

Senior Member (Voting rights)

Background​

Persistent physical symptoms are among the most common and challenging presentations in general practice, whether investigations are normal or reveal pathology insufficient to explain their severity. Patients and clinicians alike often experience these presentations as a dead end. Traditional biomedical models assume that symptoms passively reflect structural pathology, and that normal test results imply the absence of illness.

Objectives​

To introduce predictive processing as a theoretical framework for understanding persistent physical symptoms in general practice, and to outline its implications for clinical explanation and management.

Methods​

This opinion paper draws on the predictive processing and active inference literature to develop a clinically applicable account of symptom perception, illustrated with clinical scenarios from general practice.

Results​

Predictive processing proposes that symptoms are active perceptual constructions, generated by the brain as it infers the state of the body from incoming signals and prior beliefs. When prior beliefs of danger or irreversibility are strong, and peripheral signals are weak or ambiguous, these prior beliefs can dominate perception, producing substantial suffering in the absence of clear pathology. This framework explains symptom–test discordance and reframes normal results: they do not end clinical responsibility, but shift it towards modifying symptom-sustaining priors towards expectations of safety and recovery.

Conclusion​

For the general practitioner, this framework provides a coherent scientific rationale for explanation, continuity of care, expectation management, cognitive approaches and graded activity. These interventions emerge as core clinical tools, not consolation, when “nothing is found”.

KEY MESSAGES​

  • Predictive processing explains why symptoms may persist despite normal or inconclusive clinical investigations.
  • It accounts for discordance between reported symptoms and test results, and for variation between patients and over time.
  • General practice interventions—expectation management, continuity of care, and graded activity—offer meaningful care beyond establishing a diagnosis.

Open access
 
  • Predictive processing explains why symptoms may persist despite normal or inconclusive clinical investigations.
  • It accounts for discordance between reported symptoms and test results, and for variation between patients and over time.
  • General practice interventions—expectation management, continuity of care, and graded activity—offer meaningful care beyond establishing a diagnosis.
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No, no, and no.
 
“For some patients the language of ‘brain processes’ is acceptable; others may respond better to metaphors such as a ‘sensitised alarm system’ or ‘symptom sensitivity’ that has become heightened. The crucial message is that this process is real, bodily and potentially reversible, not a sign that symptoms are imagined or fabricated.”​

What I still don’t understand after reading this paper is how the language of metaphor differs from the language of ‘brain process’, except in style? What exactly is this brain process? Wouldn’t a theory like this be easy to prove experimentally? Something that the authors don’t address is that the vast majority of people expect to recover completely after an infection, as do their doctors, families, and employers. Yet some of them don’t.

“When clinicians feel uncertain or pressured in the face of persistent symptoms and normal test results, the default response is often to repeat or extend investigations. From a predictive processing perspective this may be counterproductive: each additional test implicitly reinforces the prior that something serious may still have been missed, maintaining illness-sustaining beliefs in both patient and clinician.”​

Oh brother. This theory is a boon for insurance companies. You might as well argue the opposite: refusal to investigate further reinforces the belief that the patient’s concerns are being dismissed and keeps them from updating their ‘priors’. The preceding paragraph literally argues:

“Explanation becomes a therapeutic act, not merely a way of delivering test results. A useful starting point is to summarise clearly what has and has not been found, and what has been actively ruled out. This can begin to update priors of danger…”​
 
The paper said:
Patients and clinicians alike often experience these presentations as a dead end
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The idea that our experiences are in any way similar is ridiculously offensive. It's not a dead-end to clinicians, they just move on to the next and forget all about it while our lives fall further into despair and misery. It's precisely because the stakes imbalance is so wide that nothing has improved in decades. We are not in this together at all, there is literally no common we, it's cruel, indifferent systems vs people with zero influence who have been cast out of society, rejected as worthless by systems that clearly see us as being beneath their concern.
Predictive processing proposes that symptoms are active perceptual constructions, generated by the brain as it infers the state of the body from incoming signals and prior beliefs. When prior beliefs of danger or irreversibility are strong, and peripheral signals are weak or ambiguous, these prior beliefs can dominate perception, producing substantial suffering in the absence of clear pathology. This framework explains symptom–test discordance and reframes normal results: they do not end clinical responsibility, but shift it towards modifying symptom-sustaining priors towards expectations of safety and recovery.
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Would you look at that, exact same junk as before, presented as "novel", which is the exact same junk as before. And of course ending clinical responsibility, hell voiding it entirely, is and has always been the explicit goal.

This would be considered plagiarism if it wasn't for the fact that no one in the industry seems to care that it's always the same recycled nonsense. For some reason they use "processing" instead of coding, so pretty much did a simple search-and-replace with predictive coding and clearly journals don't care about perpetuating nonsense.

We can already apply a simple LLM test: would an LLM do better than this? If easily so, and this is the case here, it's clearly not worth publishing. But of course the only goal here is to void clinical responsibility entirely, they simply have goals that are antagonistic to our welfare.
 
lelelemonade said:
What I still don’t understand after reading this paper is how the language of metaphor differs from the language of ‘brain process’, except in style? What exactly is this brain process? Wouldn’t a theory like this be easy to prove experimentally? Something that the authors don’t address is that the vast majority of people expect to recover completely after an infection, as do their doctors, families, and employers. Yet some of them don’t.
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The standard of evidence in psychosomatic ideology has always been: if it can be imagined and it conforms with the traditional conversion disorder model, then it must be correct. It simply has to be imagined as being possible to be considered certain. They can simply imagine that some people might behave this way, and make life-and-death decisions about tens of millions without ever caring about the outcomes.

This is why it never matters when evidence contradicts the model. The model is entirely made out of hallucinations, and only perpetuates because of failed systems that rely on those hallucinations as excuses for their failures.

The narratives are entirely for them, to make them feel not only good about failing, but to feel better the worse they fail because they inverted reality to make it all work.
 
Yet another «imagine a world» paper that presents opinions as facts. Pretty much all of their references are other opinion pieces.

It’s even made by AI:
The authors used AI-assisted tools during the preparation of this manuscript, specifically for iterative drafting, structural editing and consistency checking of the text. All scientific content, clinical judgements and conclusions are the sole responsibility of the authors, who reviewed and edited all AI-assisted output and take full responsibility for the final manuscript.
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