Background Lower urinary tract obstruction (LUTO) is a rare but critical fetal diagnosis. Different ultrasound markers have been reported with varying sensitivity and specificity. Aims The objective of this systematic review and meta‐analysis was to identify the diagnostic accuracy of ultrasound markers for LUTO. Materials and Methods We performed a systematic literature review of studies reporting on fetuses with hydronephrosis or a prenatally suspected and/or postnatally confirmed diagnosis of LUTO. Bayesian bivariate random effects meta‐analytic models were fitted, and we calculated posterior means and 95% credible intervals for the pooled diagnostic odds ratio (DOR). Results A total of 36,189 studies were identified; 636 studies were available for full text review and a total of 42 studies were included in the Bayesian meta‐analysis. Among the ultrasound signs assessed, megacystis (DOR 49.15, [15.28, 177.44]), bilateral hydroureteronephrosis (DOR 41.33, [13.36,164.83]), bladder thickening (DOR 13.73, [1.23, 115.20]), bilateral hydronephrosis (DOR 8.36 [3.17, 21.91]), male sex (DOR 8.08 [3.05, 22.82]), oligo‐ or anhydramnios (DOR 7.75 [4.23, 14.46]), and urinoma (DOR 7.47 [1.14, 33.18]) were found to be predictive of LUTO (Table 1). The predictive sensitivities and specificities however are low and wide study heterogeneity existed. Discussion Classically, LUTO is suspected in the presence of prenatally detected megacystis with a dilated posterior urethra (i.e., the keyhole sign), and bilateral hydroureteronephrosis. However, keyhole sign has been found to have modest diagnostic performance in predicting the presence of LUTO in the literature which we confirmed in our analysis. The surprisingly low specificity may be influenced by several factors, including the degree of obstruction, and the diligence of the sonographer at searching for and documenting it during the scan. As a result, providers should consider this when establishing the differential for a fetus with hydronephrosis as the presence or absence of keyhole sign does not reliably rule in or rule out LUTO. Conclusions Megacystis, bilateral hydroureteronephrosis and bladder wall thickening are the most accurate predictors of LUTO. Given the significant consequences of a missed LUTO diagnosis, clinicians providing counselling for prenatal hydronephrosis should maintain a low threshold for considering LUTO as part of the differential diagnosis.
Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note processing; however, broad deployment of ML for this task is restricted by the scarcity and imbalance of labeled training data. In this work we present a method that improves a model’s ability to generalize through novel data augmentation techniques that utilizes information from biomedical ontologies in the form of related medical concepts, as well as global context information within the medical note. We train our model on a public dataset (MIMIC III) and test its performance on automatically generated and hand-labelled datasets from different sources (MIMIC III, CASI, i2b2). Together, these techniques boost the accuracy of abbreviation disambiguation by up to 17% on hand-labeled data, without sacrificing performance on a held-out test set from MIMIC III.
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