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We discovered a variation of rat sciatic nerve anatomy as an incidental finding during the anatomical exploration of the nerve lesion site in a rat neuropathic pain model. To confirm the composition and distribution of rat sciatic nerve, macroscopic anatomical investigation was performed in both left and right sides in 24 adult Sprague-Dawley rats. In all rats, the L4 and L5 spinal nerves were fused tightly to form the sciatic nerve. However, the L6 spinal nerve did not fuse with this nerve completely as a part of the sciatic nerve, but rather sent a thin branch to it in 13 rats (54%), whereas in the remaining 11 rats (46%), L6 ran separately along with the sciatic nerve. Also, the L3 spinal nerve sent a thin branch to the L4 spinal nerve or sciatic nerve in 6 rats (25%). We conclude that the components of sciatic nerve in Sprague-Dawley rats vary from L3 to L6; however, the major components are L4 and L5 macroscopically. This finding is in contrast to the standard textbooks of rat anatomy which describe the sciatic nerve as having major contributions from L4, L5, and L6.

BackgroundThe triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.MethodsDispatch, ambulance, and hospital data were collected in one Swedish region from 2016–2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.ResultsA total of 38203 patients were included from 2016–2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51–0.66, while those for NEWS ranged from 0.66–0.85. Concordance ranged from 0.70–0.79 for risk scores based only on dispatch data, and 0.79–0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.ConclusionsMachine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.