A Comparison of Lichen Sclerosus and Vulvodynia Content Across Social Media Platforms: What Is Trending Over Time.

OBJECTIVES: Vulvovaginal diseases are common gynecologic complaints and patients often turn to social media (SM) for medical information. The objective of this study is to examine vulvovaginal content on SM and how it has changed over time. MATERIALS AND METHODS: Four SM platforms were searched (i.e., Facebook, Instagram, Twitter, and YouTube) at 2 time points from March 30 to May 7, 2021, and again from November 24 to December 10, 2022. Newer SM platforms became popular during this time interval and thus TikTok and Reddit were included in the search in 2022. This study focused on 2 common vulvovaginal conditions: lichen sclerosus and vulvodynia. The SM platforms were searched for content on these conditions and the type of content, language, and country of origin were assessed. RESULTS: A total of 1228 SM accounts, posts, and pages were assessed. Lichen sclerosus content on SM was mostly informational (32.6%), whereas vulvodynia content was mostly personal experience (30.5%). Patient support groups were significantly more popular in 2021 compared with 2022 and professional groups were more common in 2022 compared with 2021 ( p < .001). Overall, Facebook and Instagram consisted mostly of patient support groups, YouTube had both informational and professional videos, TikTok had mostly personal experiences and healthcare professional videos, and Reddit was mostly discussions about patient personal experiences. CONCLUSIONS: The current study highlights the content and quantifies user engagement of lichen sclerosus and vulvodynia on SM.

Systematic review of the safety, immunogenicity, and effectiveness of COVID-19 vaccines in pregnant and lactating individuals and their infants.

BACKGROUND: There is significant risk of complications and vulnerability to severe COVID-19 disease in pregnancy, yet hesitancy exists around COVID-19 vaccination during pregnancy and lactation. OBJECTIVE: To summarize the safety, immunogenicity, and effectiveness of COVID-19 vaccines in pregnancy and lactation. SEARCH STRATEGY: A systematic search of MEDLINE, Embase, PubMed, medRxiv, and bioRxiv. SELECTION CRITERIA: Identified original studies published on pregnant and/or lactating individuals who received one or more doses of a COVID-19 vaccine. DATA COLLECTION AND ANALYSIS: A descriptive summary organized by safety, immunogenicity, and effectiveness outcomes of COVID-19 vaccination in pregnancy and lactation. MAIN RESULTS: In total, 23 studies were identified. Humoral response and functional immunity were interrogated and found. Increasing placental transfer ratios in cord blood were associated with increasing time from the first vaccine dose to delivery. Safety data indicated that pregnant and lactating populations experienced vaccine-related reactions at similar rates to the general population. No increased risk of adverse obstetrical or neonatal outcomes were reported. One study demonstrated that pregnant individuals were less likely to experience COVID-19 when vaccinated. CONCLUSION: COVID-19 vaccination in pregnant and lactating individuals is immunogenic, does not cause significant vaccine-related adverse events or obstetrical and neonatal outcomes, and is effective in preventing COVID-19 disease.

Clinical use of artificial intelligence in endometriosis: a scoping review.

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

Accuracy of a Self-Administered Online Cognitive Assessment in Detecting Amnestic Mild Cognitive Impairment.

OBJECTIVES: Our aim was to validate the online Brain Health Assessment (BHA) for detection of amnestic mild cognitive impairment (aMCI) compared to gold-standard neuropsychological assessment. We compared the diagnostic accuracy of the BHA to the Montreal Cognitive Assessment (MoCA). METHODS: Using a cross-sectional design, community-dwelling older adults completed a neuropsychological assessment, were diagnosed as normal cognition (NC) or aMCI, and completed the BHA and MoCA. Both logistic regression (LR) and penalized logistic regression (PLR) analyses determined BHA and demographic variables predicting aMCI; MoCA variables were similarly modeled. Diagnostic accuracy was compared using area under the receiver operating characteristic curve (ROC AUC) analyses. RESULTS: Ninety-one participants met inclusion criteria (51 aMCI, 40 NC). PLR modeling for the BHA indicated Face-Name Association, Spatial Working Memory, and age-predicted aMCI (ROC AUC = 0.76; 95% confidence interval [CI]: 0.66-0.86). Optimal cut-points resulted in 21% classified as aMCI (positive), 23% negative, and 56% inconclusive. For the MoCA, digits, abstraction, delayed recall, orientation, and age predicted aMCI (ROC AUC = 0.71; 95% CI: 0.61-0.82). Optimal cut-points resulted in 22% classified positive, 8% negative, and 70% inconclusive (LR results presented within). The BHA model classified fewer participants into the inconclusive category and more as negative for aMCI, compared to the MoCA model (Stuart-Maxwell p = .004). DISCUSSION: The self-administered BHA provides similar detection of aMCI as a clinician-administered screener (MoCA), with fewer participants classified inconclusively. The BHA has the potential to save practitioners time and decrease unnecessary referrals for a comprehensive assessment to determine the presence of aMCI.

The Paradox of Dementia and Driving Cessation: "It's a Hot Topic," "Always on the Back Burner".

BACKGROUND AND OBJECTIVES: Despite the well-recognized difficulty that persons with dementia and family carers experience in the decision making and transition to nondriving, there are few interventions and resources to support them. As part of our ongoing research to develop a driving cessation toolkit that addresses this gap, we sought to examine the context-specific factors relevant to its effective implementation in settings that support older adults with dementia. RESEARCH DESIGN AND METHODS: A qualitative descriptive approach was used to explore the perspectives of Alzheimer Society (AS) staff in their work of supporting people with dementia and family carers within the context of driving cessation. Individual in-depth interviews were conducted with 15 AS staff members in 4 Canadian provinces. Data were examined using interpretative thematic analysis. RESULTS: The study results revealed an overarching paradox that despite the importance of driving cessation in people with dementia, it continues to be largely avoided at the individual and system levels. This is explored via the themes of (a) paradox of importance and avoidance identified in AS settings; (b) lack of awareness and understanding about dementia and driving among people with dementia and family carers; (c) distress and avoidance rooted in ongoing system issues; and (d) moving driving cessation to the "front burner." DISCUSSION AND IMPLICATIONS: Viewed through the emerging social health paradigm, which focuses on the social and emotional consequences of dementia, our results highlight the urgent need to mobilize our communities, medical education systems, and transportation authorities to finally resolve the dementia and driving cessation paradox.