Evidence map›Paper›PMID 23689231›Full record

ReviewNature reviews. Rheumatology2013

Post-traumatic osteoarthritis: from mouse models to clinical trials.

Christopher B Little, David J Hunter

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Rheumatology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
159citing papers in PubMed, 5 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

159 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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99 more citing papers are in PubMed but not listed here.

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

2 authors.

Christopher B LittleRaymond Purves Bone and Joint Research Laboratories, Kolling Institute of Medical Research, Institute of Bone and Joint Research, University of Sydney at Royal North Shore Hospital, St Leonards, NSW 2065, Australia. christopher.little@ sydney.edu.au
David J Hunter

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis (OA), the most common of all arthropathies, is a leading cause of disability and has a large (and growing) worldwide socioeconomic cost. Despite its burgeoning importance, translation of disease-modifying OA therapies from the laboratory into clinical practice has slowed. Differences between the OA models studied preclinically and the disease evaluated in human clinical trials contribute to this failure. Most animal models of OA induce disease through surgical or mechanical disruption of joint biomechanics in young individuals rather than the spontaneous development of age-associated disease. This instability-induced joint disease in animals best models the arthritis that develops in humans after an injurious event, known as post-traumatic OA (PTOA). Studies in genetically modified mice suggest that PTOA has a distinct molecular pathophysiology compared with that of spontaneous OA, which might explain the poor translation from preclinical to clinical OA therapeutic trials. This Review summarizes the latest data on potential molecular targets for PTOA prevention and modification derived from studies in genetically modified mice, and describes their validation in preclinical therapeutic trials. This article focuses on how these findings might best be translated to humans, and identifies the potential challenges to successful implementation of clinical trials of disease-modifying drugs for PTOA.

Indexed as

Disease Models, AnimalTranslational Research, BiomedicalAnimalsAntirheumatic AgentsBiomarkersGenetic EngineeringHumansMiceOsteoarthritisWounds and InjuriesAntirheumatic AgentsBiomarkers

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.