ReviewCommunications medicine2026
Assessing the translational value of animal models in developing pain-targeting therapies for clinical osteoarthritis.
Review in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
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Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoarthritis (OA) is a highly prevalent and disabling disease, with pain as its most debilitating symptom. Despite extensive preclinical research, no therapy has successfully translated from animal models into routine clinical use. Commonly used animal models (e.g., acute, surgically, or chemically induced OA in young, single-sex animals) often fail to reflect the chronic and multifactorial human condition, which is often seen in older, postmenopausal women with comorbidities and mixed nociceptive-neuropathic pain. This review aims to identify animal models and pain assessment methods for predicting drug efficacy in OA pain management and to examine the translational gap, focusing on pain as a primary endpoint in animal and human clinical studies. Ultimately, we emphasize the need for phenotype-specific, clinically relevant models to improve translational validity and accelerate the development of effective OA pain therapies.
Identifiers
42850371What 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.