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Objective. To assess the value of the history and physical examination findings in the diagnosis of symptomatic degenerative lumbar spinal stenosis (LSS).Methods. The study was performed in 3 specialty clinics, and included patients with low back pain who were at least age 40. Findings from a standardized history and physical examination were compared with the diagnostic impression of expert attending clinicians. Imaging studies were available in 88% of those with LSS, and the findings further supported the diagnosis of LSS in each case. The sensitivity, specificity, and likelihood ratio associated with each history and physical examination finding were calculated in bivariate analyses, and independent correlates of LSS were identified with multivariate analyses.Results. Ninety-three patients were evaluated. History findings most strongly associated with the diagnosis of LSS (likelihood ratio 12) were greater age, severe lower-extremity pain, and absence of pain when seated. Physical examination findings most strongly associated with the diagnosis were wide-based gait, abnormal Romberg test result, thigh pain following 30 seconds of lumbar extension, and neuromuscular defi-

Patients over 60 years of age with a dens fracture had a higher complication rate and lower cervical range of motion when treated conservatively with a halo. Final functional outcome and overall pain levels, however, did not differ significantly by age group or treatment modality.

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One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational modeling revealed strong correlations between transcription and cytogenetic changes, particularly loss of chromosome 1p, in malignant tumors. Applying our classifier to data from previous studies also resolved certain anomalies entailed by grouping tumors by WHO grade. Accurate classification will therefore elucidate meningioma biology as well as improve diagnosis and prognosis.