Background: Non-convulsive status epilepticus (NCSE) is status epilepticus without obvious tonic-clonic activity. Patients with NCSE have altered mental state. An EEG is needed to confirm the diagnosis, but obtaining an EEG on every patient with altered mental state is not practical. Objective: To determine whether clinical features could be used to predict which patients were more likely to be in NCSE and thus in need of an urgent EEG. Methods: Over a six month period, all patients for whom an urgent EEG was ordered to identify NCSE were enrolled. Neurology residents examined the patients and filled out a questionnaire without knowledge of the EEG results. The patients were divided into two groups, NCSE and non-NCSE, depending on the EEG result. The clinical features were compared between the two groups. The sensitivity and specificity of the features were calculated. Results: 48 patients were enrolled, 12 in NCSE and 36 not in NCSE. Remote risk factors for seizures, severely impaired mental state, and ocular movement abnormalities were seen significantly more often in the NCSE group. The combined sensitivity of remote risk factors for seizures and ocular movement abnormalities was 100%. Conclusions: There are certain clinical features that are more likely to be present in patients in NCSE compared with other types of encephalopathy. Either remote risk factors for seizures or ocular movement abnormalities were seen in all patients in NCSE. These features may be used to select which patients should have an urgent EEG.

Comparisons between the East Boston Memory Test (EBMT), a brief verbal memory measure used in epidemiological studies with dementia, selected Wechsler Memory Scale-Revised (WMS-R) subtests, and the Mini-Mental State Examination (MMSE) were investigated with 23 geriatric patients diagnosed with dementia. Significant correlations between the EBMT and WMS-R verbal subtests were predicted and occurred (r = .42 to .64). A five minute EBMT recall correlated most highly with the WMS-R Logical Memory subtests. The sensitivity of the EBMT in detecting cognitive impairment was investigated and compared with the sensitivity of the MMSE. The EBMT correctly classified 78% of subjects, compared to a 70% correct classification rate with the MMSE. Implications of these findings and suggestions for future research directions are discussed.

The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractNumerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted.In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge.Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi-automated approach for the assessment of precipitation forecast ensemble 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions. *Manuscript Click here to view linked References

Depressive symptoms had a deleterious impact on outcome. Remediation of symptoms during rehabilitation significantly improved outcomes.

The results support the effectiveness of a continuum of care for acquired brain injury individuals beyond hospitalization and acute in-hospital rehabilitation. It is particularly noteworthy that reduction in disability was achieved for all levels of programming even with participants whose onset to admission exceeded 7 years post-injury.