RESUMEN

Mismeasurement of a dichotomous outcome yields an unbiased risk ratio estimate when there are no false positive cases (perfect specificity) and when sensitivity is non-differential with respect to exposure status. In studies where these conditions are expected, quantitative bias analysis may be considered unnecessary. We conducted a simulation study to explore the robustness of this special case to small departures from perfect specificity and stochastic departures from non-differential sensitivity. We observed substantial bias of the risk ratio with specificity values as high at 99.8%. The magnitude of bias increased directly with the true underlying risk ratio and was markedly stronger at lower baseline risk. Stochastic departure from non-differential sensitivity also resulted in substantial bias in most simulated scenarios; downward bias prevailed when sensitivity was higher among unexposed compared with exposed, and upward bias prevailed when sensitivity was higher among exposed compared with unexposed. Our results show that seemingly innocuous departures from perfect specificity (e.g., 0.2%) and from non-differential sensitivity can yield substantial bias of the risk ratio under outcome misclassification. We present a web tool permitting easy exploration of this bias mechanism under user-specifiable study scenarios.

RESUMEN

Ovarian torsion is a gynecologic emergency which, while rare during pregnancy, is associated with increased risk during pregnancy. Most torsions during pregnancy occur during the first and second trimester, with only 10.5% of cases reported during the third trimester. A 35-year-old woman at 32 weeks and 2 days of gestation presented with right lower quadrant abdominal pain. Transvaginal ultrasound demonstrated a large right ovarian cyst and decreased flow on color Doppler consistent with ovarian torsion. The diagnosis was confirmed via diagnostic laparoscopy with direct visualization of the necrotic, edematous ovarian cyst and pedicle, which had been torsed twice. The pedicle was detorsed and the necrotic cyst was resected while sparing as much of the normal ovary as possible. The patient provided written consent for publication of this case report. Data supports that laparoscopy is a safe and reasonable treatment for ovarian torsion during pregnancy. It is associated with shorter hospital stays and fewer postoperative complications without increasing the risk of obstetric or neonatal complications. Much of this data, however, is obtained from case reports of torsions during the first and second trimester. The incidence of third trimester ovarian torsion is estimated to be 5-10% of torsion cases that occur in pregnancy. This case demonstrates a successful laparoscopic treatment of a third-trimester torsion in a woman who went on to deliver a healthy baby girl by spontaneous vaginal delivery.

RESUMEN

While the brain's functional network architecture is largely conserved between resting and task states, small but significant changes in functional connectivity support complex cognition. In this study, we used a modified Raven's Progressive Matrices Task to examine symbolic and perceptual reasoning in human participants undergoing fMRI scanning. Previously, studies have focused predominantly on discrete symbolic versions of matrix reasoning, even though the first few trials of the Raven's Advanced Progressive Matrices task consist of continuous perceptual stimuli. Our analysis examined the activation patterns and functional reconfiguration of brain networks associated with resting state and both symbolic and perceptual reasoning. We found that frontoparietal networks, including the cognitive control and dorsal attention networks, were significantly activated during abstract reasoning. We determined that these same task-active regions exhibited flexibly-reconfigured functional connectivity when transitioning from resting state to the abstract reasoning task. Conversely, we showed that a stable network core of regions in default and somatomotor networks was maintained across both resting and task states. We propose that these regionally-specific changes in the functional connectivity of frontoparietal networks puts the brain in a "task-ready" state, facilitating efficient task-based activation.

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RESUMEN

Variations in the functional connectivity of large-scale cortical brain networks may explain individual differences in learning ability. We used a dynamic network analysis of fMRI data to identify changes in functional brain networks that are associated with context-dependent rule learning. During fMRI scanning, naïve subjects performed a cognitive task designed to test their ability to learn context-dependent rules. Notably, subjects were given minimal instructions about the task prior to scanning. We identified several key network characteristics associated with fast and accurate rule learning. First, consistent with the formation of stable functional networks, a dynamic community detection analysis revealed regionally specific reductions in flexible switching between different functional communities in successful learners. Second, successful rule learners showed decreased centrality of ventral attention regions and increased assortative mixing of cognitive control regions as the rules were learned. Finally, successful subjects showed greater decoupling of default and attention communities throughout the entire task, whereas ventral attention and cognitive control regions became more connected during learning. Overall, the results support a framework by which a stable ventral attention community and more flexible cognitive control community support sustained attention and the formation of rule representations in successful learners.

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