Evidence map›Paper›PMID 36303889›Full record

ArticleFrontiers in pain research (Lausanne, Switzerland)2022

A Bayesian model for chronic pain.

Anna-Lena Eckert, Kathrin Pabst, Dominik M Endres

Abstract read
In one paragraph

Article in Frontiers in pain research (Lausanne, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

  1. Pain and functional neurological disorder: a systematic review and meta-analysis.Journal of neurology, neurosurgery, and psychiatry · 2024
    Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. RDoC Mechanisms Underlying Bodily Symptoms in Psychiatry: A Scoping Review.Psychology research and behavior management · 2026
    Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Beyond pain privacy and pain meters: a new vision for pain biomarkers.Frontiers in pain research (Lausanne, Switzerland) · 2024
    Article
  16. Article
  17. Landau model for illustrating the learning and unlearning process of nociplastic pain.Frontiers in pain research (Lausanne, Switzerland) · 2024
    Article
  18. Review
  19. Article
  20. Review
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

3 authors.

Anna-Lena EckertTheoretical Cognitive Science Group, Department of Psychology, Philipps-University Marburg, Marburg, Germany.
Kathrin PabstTheoretical Cognitive Science Group, Department of Psychology, Philipps-University Marburg, Marburg, Germany.
Dominik M EndresTheoretical Cognitive Science Group, Department of Psychology, Philipps-University Marburg, Marburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The perceiving mind constructs our coherent and embodied experience of the world from noisy, ambiguous and multi-modal sensory information. In this paper, we adopt the perspective that the experience of pain may similarly be the result of a probabilistic, inferential process. Prior beliefs about pain, learned from past experiences, are combined with incoming sensory information in a Bayesian manner to give rise to pain perception. Chronic pain emerges when prior beliefs and likelihoods are biased towards inferring pain from a wide range of sensory data that would otherwise be perceived as harmless. We present a computational model of interoceptive inference and pain experience. It is based on a Bayesian graphical network which comprises a hidden layer, representing the inferred pain state; and an observable layer, representing current sensory information. Within the hidden layer, pain states are inferred from a combination of priors

Indexed as

Bayesian inferencebelief-propagationchronic paincomputational psychiatryfree energygraphical modelsinteroceptionpredictive coding

Identifiers

PMID36303889
PMCPMC9595216

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.