ArticleNPJ digital medicine2026
Pain assessment using physiological responses/markers in different types of pain: a scoping review.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Sensor-Based Monitoring of Knee Osteoarthritis Symptoms in Free-Living Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Hippocampal neuroinflammation and altered peripheral neurobiological protein profile in experimental arthritis and systemic juvenile idiopathic arthritis.EBioMedicine · 2026Article
- A narrative review of AI monitoring in postoperative pain management and functional rehabilitation for spinal cord injury.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain is a complex multidimensional experience that integrates sensory and emotional components, presenting significant challenges for accurate assessment in clinical practice. Traditional methods of pain evaluation rely on subjective self-reporting and each individual's ability to communicate their pain experience. In light of the effect of pain on the Autonomic Nervous System, researchers are interested in developing objective assessment techniques using physiological signals. This paper outlines the latest advances in pain biomarkers and machine learning methods for assessing pain using physiological signals, highlighting the growing interest and unmet demand in this area. A comprehensive literature review was conducted, covering studies between 2014 and 2024. The discussion is organised into two areas: first, an analysis of the variations in signal feature behaviour across different pain types, and second, a review of the current state-of-the-art models for pain assessment developed using classical machine learning and deep learning techniques.
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.