Three research papers published in the Experimental Neurology journal within a single month!

This accomplishment is a testament to the hard work, dedication, and innovative spirit of our researchers.

Samuel Håkansson and external page Miklovana Tuci et al. conducted a systematic review on data-driven applications for spinal cord injury (SCI) recovery predictions, revealing that simple prediction models are most common, with a growing trend toward complexity. The authors urge the community to enhance reporting practices, share code and data for better reproducibility, and emphasize the need for external validation and rigorous train-test splits 🔗external page https://www.sciencedirect.com/science/article/pii/S0014488624002395

Sarah Brüningk et al. introduced a well-elaborated machine learning concept - historical twins by kNN regression - which is highly interpretable, transparent and validated to make accurate recovery predictions at the level of segmental motor scores in traumatic spinal cord injury. 🔗external page https://www.sciencedirect.com/science/article/pii/S0014488624002310?via%3Dihub

external page Jan Matthias et al. explored the potential of routine serological markers in predicting neurological outcomes in SCI. They found that including these markers did not improve prediction of Lower Extremity Motor Scores (LEMS) 52 weeks after SCI. However, stratifying predictive models by very acute LEMS improved prediction performance 🔗external page https://www.sciencedirect.com/science/article/pii/S0014488624002449

A huge thank you to everyone involved in this journey—your collaboration and passion made this possible!

Stay tuned as we continue to push the boundaries of research and innovation.