Background:. Identification of nerve root involvement (NRI) in patients with low back-related leg pain (LBLP) can be challenging. Diagnostic models have mainly been developed in secondary care with conflicting reference standards and predictor selection. This study aims to ascertain which cluster of items from clinical assessment best identify NRI in primary care consulters with LBLP. Methods:. Cross-sectional data on 395 LBLP consulters were analysed. Potential NRI indicators were seven clinical assessment items. Two definitions of NRI formed the reference standards: (i) high confidence (≥80%) NRI clinical diagnosis (ii) high confidence (≥80%) NRI clinical diagnosis with confirmatory magnetic resonance imaging (MRI) findings. Multivariable logistic regression models were constructed and compared for both reference standards.
To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism.Aims
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