What clinical prediction tools can help identify patients with rhabdomyolysis who are at risk of developing acute kidney injury?
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Zenodo
2025
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| _version_ | 1866901632092995584 |
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| author | Tripdatabase |
| author_facet | Tripdatabase |
| contents | Current evidence highlights certain risk factors and predictive models for AKI in rhabdomyolysis, but further validation across populations and settings is needed for clinical utility. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17700309 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | What clinical prediction tools can help identify patients with rhabdomyolysis who are at risk of developing acute kidney injury? Tripdatabase clinical prediction tools help identify patients rhabdomyolysis who risk developing acute kidney injury Current evidence highlights certain risk factors and predictive models for AKI in rhabdomyolysis, but further validation across populations and settings is needed for clinical utility. |
| title | What clinical prediction tools can help identify patients with rhabdomyolysis who are at risk of developing acute kidney injury? |
| topic | clinical prediction tools help identify patients rhabdomyolysis who risk developing acute kidney injury |
| url | https://doi.org/10.5281/zenodo.17700309 |