The rapid growth of a pleomorphic adenoma of the parotid gland in the third trimester of pregnancy

<p>Abstract</p> <p>Introduction</p> <p>We report a case highlighting the multidisciplinary management of a giant pleomorphic adenoma of the parotid gland that showed rapid growth in the third trimester of pregnancy.</p> <p>Case presentation</p> <p>A 43-year-old Caucasian woman presented in her 32nd week of gestation with a tumor of the parotid gland. Ultrasonography of her neck showed a parotid lesion of 40 × 30 × 27.5 mm. A follow-up magnetic resonance imaging scan of the neck four weeks later revealed that the tumor had grown to 70 × 60 × 60 mm, reaching the parapharyngeal space with marked obstruction of the oropharynx of about 50%. After discussing the case with our multidisciplinary tumor board and the gynecologists it was decided to deliver the baby by caesarean section in the 38th week of gestation, and then to perform a surgical resection of the tumor.</p> <p>Conclusion</p> <p>Indications for early surgical intervention of similar cases should be discussed on an individual patient basis in a multidisciplinary setting.</p

Twinning superlattices in indium phosphide nanowires

Here, we show that we control the crystal structure of indium phosphide (InP) nanowires by impurity dopants. We have found that zinc decreases the activation barrier for 2D nucleation growth of zinc-blende InP and therefore promotes the InP nanowires to crystallise in the zinc blende, instead of the commonly found wurtzite crystal structure. More importantly, we demonstrate that we can, by controlling the crystal structure, induce twinning superlattices with long-range order in InP nanowires. We can tune the spacing of the superlattices by the wire diameter and the zinc concentration and present a model based on the cross-sectional shape of the zinc-blende InP nanowires to quantitatively explain the formation of the periodic twinning.Comment: 18 pages, 4 figure

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Pleomorphic adenoma of the vulva, clinical reminder of a rare occurrence

Pleomorphic adenoma, also known as mixed tumor, is a benign tumor which typically presents as a painless and persistent mass. The majority of pleomorphic adenomas involve the salivary glands, most commonly the parotid gland. Other sites include breast and skin. It is a rare tumor in the vulva. In this article we are reporting a case of pleomorphic adenoma of labia with characteristic pathologic and clinical findings, as reminder of a common benign neoplasm occurring with rare locality

Reliability and Validity of the KIPPPI: An Early Detection Tool for Psychosocial Problems in Toddlers

Background: The KIPPPI (Brief Instrument Psychological and Pedagogical Problem Inventory) is a Dutch questionnaire that measures psychosocial and pedagogical problems in 2-year olds and consists of a KIPPPI Total score, Wellbeing scale, Competence scale, and Autonomy scale. This study examined the reliability, validity, screening accuracy and clinical application of the KIPPPI. Methods: Parents of 5959 2-year-old children in the Rotterdam area, the Netherlands, were invited to participate in the study. Parents of 3164 children (53.1% of all invited parents) completed the questionnaire. The internal consistency was evaluated and in subsamples the test-retest reliability and concurrent validity with regard to the Child Behavioral Checklist (CBCL). Discriminative validity was evaluated by comparing scores of parents who worried about their child's upbringing and parent's that did not. Screening accuracy of the KIPPPI was evaluated against the CBCL by calculating the Receiver Operating Characteristic (ROC) curves. The clinical application was evaluated by the relation between KIPPPI scores and the clinical decision made by the child health professionals. Results: Psychometric properties of the KIPPPI Total score, Wellbeing scale, Competence scale and Autonomy scale were respectively: Cronbach's alphas: 0.88, 0.86, 0.83, 0.58. Test-rete

Methodological quality of test accuracy studies included in systematic reviews in obstetrics and gynaecology: sources of bias

<p>Abstract</p> <p>Background</p> <p>Obstetrics and gynaecology have seen rapid growth in the development of new tests with research on these tests presented as diagnostic accuracy studies. To avoid errors in judgement it is important that the methodology of these studies is such that bias is minimised. Our objective was to determine the methodological quality of test accuracy studies in obstetrics and gynaecology using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist and to assess sources of bias.</p> <p>Methods</p> <p>A prospective protocol was developed to assess the impact of QUADAS on ten systematic reviews performed over the period 2004-2007.We investigated whether there was an improvement in study quality since the introduction of QUADAS, whether a correlation existed between study sample size, country of origin of study and its quality. We also investigated whether there was a correlation between reporting and methodological quality and by the use of meta-regression analyses explored for items of quality that were associated with bias.</p> <p>Results</p> <p>A total of 300 studies were included. The overall quality of included studies was poor (> 50% compliance with 57.1% of quality items). However, the mean compliance with QUADAS showed an improvement post-publication of QUADAS (54.9% versus 61.4% p = 0.002). There was no correlation with study sample size. Gynaecology studies published from the United States of America showed higher quality (USA versus Western Europe p = 0.002; USA versus Asia p = 0.004). Meta-regression analysis showed that no individual quality item had a significant impact on accuracy. There was an association between reporting and methodological quality (r = 0.51 p < 0.0001 for obstetrics and r = 0.56 p < 0.0001 for gynaecology).</p> <p>Conclusions</p> <p>A combination of poor methodological quality and poor reporting affects the inferences that can be drawn from test accuracy studies. Further compliance with quality checklists is required to ensure that bias is minimised.</p

Meta-DiSc: a software for meta-analysis of test accuracy data

BACKGROUND: Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. RESULTS: Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. CONCLUSION: Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at