ArticleNeurobiology of pain (Cambridge, Mass.)
Towards a deeper understanding of pain: How machine learning and deep learning algorithms are needed to provide the next generation of pain medicine for use in the clinic.
Article in Neurobiology of pain (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 citations in OpenAlex.
- Chronic Pain and Opioids in the Elderly: Treating the Brain, Not Just the Body.International journal of environmental research and public health · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As our definition of pain evolves, the factors implicit in defining and predicting pain status grow. These factors each have unique data characteristics and their outcomes each have unique target attributes. The clinical characterization of pain does not, as defined in the most recent IASP definition, require any tissue pathology, suggesting that the experience of pain can be uniquely psychological in nature. Predicting a persons pain status may be optimized through integration of multiple independent observations; however, how they are integrated has direct relevance towards predicting chronic pain development, clinical application, and research investigation. The current challenge is to find clinically-mindful ways of integrating clinical pain rating scales with neuroimaging of the peripheral and central nervous system with the biopsychocial environment and improving our capacity for diagnostic flexibility and knowledge translation through data modeling. This commentary addresses how our current knowledge of pain phenotypes and risk factors interacts with statistical models and how we can proceed forward in a clinically responsible way.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.