Clinical presentation of a traumatic cervical spine disc rupture in alpine sports: a case report

ABSTRACT: Isolated non-skeletal injuries of the cervical spine are rare and frequently missed. Different evaluation algorithms for C-spine injuries, such as the Canadian C-spine Rule have been proposed, however with strong emphasis on excluding osseous lesions. Discoligamentary injuries may be masked by unique clinical situations presenting to the emergency physician. We report on the case of a 28-year-old patient being admitted to our emergency department after a snowboarding accident, with an assumed hyperflexion injury of the cervical spine. During the initial clinical encounter the only clinical finding the patient demonstrated, was a burning sensation in the palms bilaterally. No neck pain could be elicited and the patient was not intoxicated and did not have distracting injuries. Since the patient described a fall prevention attempt with both arms, a peripheral nerve contusion was considered as a differential diagnosis. However, a high level of suspicion and the use of sophisticated imaging (MRI and CT) of the cervical spine, ultimately led to the diagnosis of a traumatic disc rupture at the C5/6 level. The patient was subsequently treated with a ventral microdiscectomy with cage interposition and ventral plate stabilization at the C5/C6 level and could be discharged home with clearly improving symptoms and without further complications.This case underlines how clinical presentation and extent of injury can differ and it furthermore points out, that injuries contracted during alpine snow sports need to be considered high velocity injuries, thus putting the patient at risk for cervical spine trauma. In these patients, especially when presenting with an unclear neurologic pattern, the emergency doctor needs to be alert and may have to interpret rigid guidelines according to the situation. The importance of correctly using CT and MRI according to both - standardized protocols and the patient's clinical presentation - is crucial for exclusion of C-spine trauma.

Sampling-Design Effects on Properties of Species-Area Relationships - A Case Study from Estonian Dry Grassland Communities

Despite widespread use of species-area relationships (SARs), dispute remains over the most representative SAR model. Using data of small-scale SARs of Estonian dry grassland communities, we address three questions: (1) Which model describes these SARs best when known artifacts are excluded? (2) How do deviating sampling procedures (marginal instead of central position of the smaller plots in relation to the largest plot; single values instead of average values; randomly located subplots instead of nested subplots) influence the properties of the SARs? (3) Are those effects likely to bias the selection of the best model? Our general dataset consisted of 16 series of nested-plots (1 cm(2)-100 m(2), any-part system), each of which comprised five series of subplots located in the four corners and the centre of the 100-m(2) plot. Data for the three pairs of compared sampling designs were generated from this dataset by subsampling. Five function types (power, quadratic power, logarithmic, Michaelis-Menten, Lomolino) were fitted with non-linear regression. In some of the communities, we found extremely high species densities (including bryophytes and lichens), namely up to eight species in 1 cm(2) and up to 140 species in 100 m(2), which appear to be the highest documented values on these scales. For SARs constructed from nested-plot average-value data, the regular power function generally was the best model, closely followed by the quadratic power function, while the logarithmic and Michaelis-Menten functions performed poorly throughout. However, the relative fit of the latter two models increased significantly relative to the respective best model when the single-value or random-sampling method was applied, however, the power function normally remained far superior. These results confirm the hypothesis that both single-value and random-sampling approaches cause artifacts by increasing stochasticity in the data, which can lead to the selection of inappropriate models.