ReviewOrthopaedic surgery2022
Animal Models for Postoperative Implant-Related Spinal Infection.
Review in Orthopaedic surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Bioinspired and living multiscale composites for regenerative medicine in the treatment of surgical site infections.Journal of nanobiotechnology · 2026Review
- Osteomyelitis related lameness across animal species: pathophysiology, etiology, epidemiology and experimental model approaches.Frontiers in veterinary science · 2026Review
- Personalized and genetically engineered animal models for next-generation surgical implant validation.Annals of medicine and surgery (2012) · 2025Review
- 3D bio-printed scaffolds and smart implants: evaluating functional performance in animal surgery models.Annals of medicine and surgery (2012) · 2025Review
- In Vitro Bacterial Growth on Titanium Surfaces Treated with Nanosized Hydroxyapatite.Journal of functional biomaterials · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postoperative infections following implant-related spinal surgery are severe and disastrous complications for both orthopaedic surgeons and patients worldwide. They can cause neurological damage, disability, and death. To better understand the mechanism of these destructive complications and intervene in the process, further research is needed. Therefore, there is an urgent need for efficient, accurate, and easily available animal models to study the pathogenesis of spinal infections and develop new and effective anti-bacterial methods. In this paper, we provide a general review of the commonly used animal models of postoperative implant-related spinal infections, describe their advantages and disadvantages, and highlight the significance of correctly choosing the model according to the infection aspect under investigation. These models are valuable tools contributing to the better understanding of postoperative spinal infections and will continue to facilitate the invention of novel preventative and treatment strategies for patients with postoperative spinal infections. However, although they are valid and reproducible in some respects, the current animal models present certain limitations. Future ideal spinal infection animal models may assess the bacterial load of the same animal in real-time in vivo, and better mimic the human anatomy as well as surgical techniques. Strains other than Staphylococcus aureus account for a large proportion of postoperative spinal infections, and thus, the establishment of models to evaluate other types of microbial infections is expected in the future. Furthermore, novel transgenic models established on advancements in genome editing are also likely to be developed in the future.
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.