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Objective: There is no clear evidence on the risk of gestational weight loss (GWL) for individuals with obesity. Our study aimed to assess the association between GWL and adverse perinatal outcomes among individuals with obesity. Methods: This population‐based retrospective cohort study examined individuals with prepregnancy BMI ≥ 30 kg/m2 who had a singleton pregnancy, using Ontario, Canada, birth registry data from 2012 to 2020. The primary outcome was a composite of adverse outcomes, including perinatal death and neonatal morbidity. The association between GWL and risk of adverse perinatal outcomes was estimated using generalized estimating equation models and restricted cubic spline regression analysis. Stratified analysis was conducted by obesity class. Results: Of the 157,205 individuals with obesity, 6.1% experienced GWL. Compared with adequate gestational weight gain, GWL was associated with an increased risk of a composite of adverse perinatal outcomes (adjusted risk ratio: 1.31; 95% CI: 1.22–1.39). Similar results were observed in the stratified analysis. Restricted cubic spline regression analysis revealed that average weekly gestational weight changes displayed a nonlinear U‐shaped association, with a higher risk of a composite of adverse perinatal outcomes noted in the extremities, particularly toward GWL and excessive weight gain. Conclusions: Our findings suggest that GWL may increase the risk of adverse perinatal outcomes across all obesity classes. [ABSTRACT FROM AUTHOR]

In Canada, vaccine safety studies are often conducted at the provincial/territorial level where the primary data on vaccination reside. Combining health services data from multiple jurisdictions using a pooled data analytic approach would reduce the amount of time needed to detect vaccine safety signals. To determine the difference in the time it would take to identify safety signals using different proportions of the Canadian population, we conducted power and sample size calculations for a hypothetical self-controlled case series-based surveillance analysis. We used scenarios modeled after the real-world examples of myocarditis and vaccine-induced immune thrombotic thrombocytopenia (VITT) following COVID-19 vaccination as our base cases. Our calculations demonstrated that in the case of a myocarditis-type event, a pooled analysis would reduce the time needed to detect a safety signal by over 60% compared to using Ontario data alone. In the case of a VITT-type event, a pooled analysis could detect a safety signal 49 days sooner than using Ontario data alone, potentially averting as many as 30 events. Our analysis demonstrates that there is substantial value in using pan-Canadian health services data to evaluate the safety of vaccines. Efforts should be made to develop a pan-Canadian vaccine data source to allow for an earlier evaluation of suspected adverse events following immunization. [ABSTRACT FROM AUTHOR]

Cannabis use in pregnancy has increased.sup.1,2, and many women continue to use it throughout pregnancy.sup.3. With the legalization of recreational cannabis in many jurisdictions, there is concern about potentially adverse childhood outcomes related to prenatal exposure.sup.4. Using the provincial birth registry containing information on cannabis use during pregnancy, we perform a retrospective analysis of all live births in Ontario, Canada, between 1 April 2007 and 31 March 2012. We link pregnancy and birth data to provincial health administrative databases to ascertain child neurodevelopmental outcomes. We use matching techniques to control for confounding and Cox proportional hazards regression models to examine associations between prenatal cannabis use and child neurodevelopment. We find an association between maternal cannabis use in pregnancy and the incidence of autism spectrum disorder in the offspring. The incidence of autism spectrum disorder diagnosis was 4.00 per 1,000 person-years among children with exposure compared to 2.42 among unexposed children, and the fully adjusted hazard ratio was 1.51 (95% confidence interval: 1.17-1.96) in the matched cohort. The incidence of intellectual disability and learning disorders was higher among offspring of mothers who use cannabis in pregnancy, although less statistically robust. We emphasize a cautious interpretation of these findings given the likelihood of residual confounding. In a cohort of nearly half a million births in Ontario, Canada, maternal cannabis use in pregnancy was associated with an increased incidence of autism spectrum disorder diagnosis in the offspring., Author(s): Daniel J. Corsi [sup.1] [sup.2] [sup.3] , Jessy Donelle [sup.4] , Ewa Sucha [sup.4] , Steven Hawken [sup.1] [sup.3] , Helen Hsu [sup.5] , Darine El-Chaâr [sup.1] [sup.6] , [...]

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions. Examples and source code can be found at: https://omni-ml.github.io/pytorch-grad-cam-anim/. [ABSTRACT FROM AUTHOR]

Background: Long COVID is highly heterogeneous, often debilitating, and may last for years after infection. The aetiology of long COVID remains uncertain. Examination of potential serological markers of long COVID, accounting for clinical covariates, may yield emergent pathophysiological insights. Methods: In adherence to PRISMA guidelines, we carried out a rapid review of the literature. We searched Medline and Embase for primary observational studies that compared IgG response in individuals who experienced COVID‐19 symptoms persisting ≥12 weeks post‐infection with those who did not. We examined relationships between serological markers and long COVID status and investigated sources of inter‐study variability, such as severity of acute illness, long COVID symptoms assessed and target antigen(s). Results: Of 8018 unique records, we identified 29 as being eligible for inclusion in synthesis. Definitions of long COVID varied. In studies that reported anti‐nucleocapsid (N) IgG (n = 10 studies; n = 989 participants in aggregate), full or partial anti‐Spike IgG (i.e. the whole trimer, S1 or S2 subgroups, or receptor binding domain, n = 19 studies; n = 2606 participants), or neutralizing response (n = 7 studies; n = 1123 participants), we did not find strong evidence to support any difference in serological markers between groups with and without persisting symptoms. However, most studies did not account for severity or level of care required during acute illness, and other potential confounders. Conclusions: Pooling of studies would enable more robust exploration of clinical and serological predictors among diverse populations. However, substantial inter‐study variations hamper comparability. Standardized reporting practices would improve the quality, consistency and comprehension of study findings. [ABSTRACT FROM AUTHOR]