Diagnostic-imaging algorithm for cervical soft disc herniation

MRI with surface coils is currently the preferred method for evaluating degenerative cervical spine disease. The differentiation between soft disc herniation and osteophytic spurs is not always obvious, however, on a 0.5 Tesla unit. The procedure of choice for soft disc herniation, MRI on a 0.5 T superconducting system associated with plain radiography of the cervical spine, in selecting patients for anterior cervical discectomy without interbody fusion (ACD), was evaluated. This prospective study comprised 100 patients with cervical radicular symptoms, not subsiding after conservative treatment. Plain radiographs were obtained for all patients. Patients without spinal instability, spondylosis, or major osteophytes on plain radiographs and without clinical findings of myelopathy underwent MRI (n = 59) on a 0.5 Tesla superconducting system. The other 41 patients underwent CT myelography. On MRI, herniation of a cervical soft disc was seen in 55 patients and the localisation corresponded well with the clinical symptoms. CT myelography showed a foraminal herniation in one of four selected patients with negative MRI. Fifty of 55 patients underwent ACD. All herniations were confirmed at operation, but in two patients there were important foraminal spurs not seen on MRI. It is concluded that 0.5 T MRI combined with plain radiographs offers an accurate, non-invasive test in the assessment of selected patients with cervical radiculopathy

On The Identification Of Nonparametric Structural Models

In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniqueness of the set of predictions of interest to the investigator. For example, predicting the effects of changes, interventions, and control. We provide sufficient and necessary conditions for identifying a set of causal predictions of the type: "Find the distribution of Y , assuming that X is controlled by external intervention", where Y and X are arbitrary variables of interest. Whenever identifiable, such predictions can be expressed in closed algebraic form, in terms of observed distributions. We also show how the identifying criteria can be verified qualitatively, by inspection, using the graphical representation of the structural model. When compared to standard identifiability tests of lin..