Support vector machine predicts the risk of C5 palsy after p
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This study aimed to predict C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables using a support vector machine (SVM) method.

Researchers looked back on 184 patients who had CM after PLF and performed a retrospective study. To classify risk factors for C5P, clinical and imaging variables were collected and imported into univariable and multivariable logistic regression analyses. The prediction model's efficiency was assessed using the accuracy (ACC), area under the receiver operating characteristic curve (AUC), and uncertainty matrices.

Results:
--Among the 184 consecutive patients, C5P occurred in 26 patients (14.13%).

--Multivariate analysis demonstrated the following 4 independent factors associated with C5P: abnormal electromyogram (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of C4–C5 foraminal stenosis (OR = 15.492).

--The SVM model achieved an area under the receiver operating characteristic curve (AUC) of 0.923 and an ACC of 0.918.

--Additionally, the confusion matrix showed the classification results of the discriminant analysis.

In conclusion, the constructed SVM model performed admirably in predicting C5P from commonly available variables.

Source: https://josr-online.biomedcentral.com/articles/10.1186/s13018-021-02476-5
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