ReviewBiomedical engineering online2015
Computational modeling of peripheral pain: a commentary.
Review in Biomedical engineering online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A Pluripotent Progression of the Gate Control System Theory of Pain - Modeling Ascending & Descending Pain Pathways as a Lotka-Volterra Coupled Control & Feedback Loop.Journal of pain research · 2025Article
- Advancing Pain Understanding and Drug Discovery: Insights from Preclinical Models and Recent Research Findings.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models.Bioengineering (Basel, Switzerland) · 2020Article
- Modeling Neural Behavior and Pain During Bladder Distention using an Agent-based Model of the Central Nucleus of the Amygdala.Spora : a journal of biomathematics · 2019Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.
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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.