SARS Antibody Testing in Children: Development of Oral Fluid Assays for IgG Measurements

We report on the first large-scale assessment of the suitability of oral fluids for detection of SARS-CoV-2 antibody obtained from healthy children attending school. The sample type (gingiva-crevicular fluid, which is a transudate of blood but is not saliva) can be self collected.

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

The Prognostic Value of NANO Scale Assessment in IDH-Wild-Type Glioblastoma Patients

BackgroundIDH-wild-type glioblastoma (GBM) is the most frequent brain-derived malignancy. Despite intense research efforts, it is still associated with a very poor prognosis. Several parameters were identified as prognostic, including general physical performance. In neuro-oncology (NO), special emphasis is put on focal deficits and cognitive (dys-)function. The Neurologic Assessment in Neuro-Oncology (NANO) scale was proposed in order to standardize the assessment of neurological performance in NO. This study evaluated whether NANO scale assessment provides prognostic information in a standardized collective of GBM patients.MethodsThe records of all GBM patients treated between 2014 and 2019 at our facility were retrospectively screened. Inclusion criteria were age over 18 years, at least 3 months postoperative follow-up, and preoperative and postoperative cranial magnetic resonance imaging. The NANO scale was assessed pre- and postoperatively as well as at 3 months follow-up. Univariate and multivariate survival analyses were carried to investigate the prognostic value.ResultsOne hundred and thirty-one patients were included. In univariate analysis, poor postoperative neurological performance (HR 1.13, p = 0.004), poor neurological performance at 3 months postsurgery (HR 1.37, p < 0.001), and neurological deterioration during follow-up (HR 1.38, p < 0.001), all assessed via the NANO scale, were associated with shorter survival. In multivariate analysis including other prognostic factors such as the extent of resection, adjuvant treatment regimen, or age, NANO scale assessment at 3 months postoperative follow-up was independently associated with survival prediction (HR 1.36, p < 0.001). The optimal NANO scale cutoff for patient stratification was 3.5 points.ConclusionNeurological performance assessment employing the NANO scale might provide prognostic information in patients suffering from GBM.