Impact of COVID-19 on access to and delivery of sexual and reproductive healthcare services in countries with universal healthcare systems: A systematic review.

OBJECTIVES: The COVID-19 pandemic has caused unforeseen impacts on sexual and reproductive healthcare (SRH) services worldwide, and the nature and prevalence of these changes have not been extensively synthesized. We sought to synthesise reported outcomes on the impact of COVID-19 on SRH access and delivery in comparable countries with universal healthcare systems. METHODS: Following PRISMA guidelines, we searched MEDLINE, Embase, PsycInfo, and CINAHL from January 1st, 2020 to June 6th, 2023. Original research was eligible for inclusion if the study reported on COVID-19 and SRH access and/or delivery. Twenty-eight OECD countries with comparable economies and universal healthcare systems were included. We extracted study characteristics, participant characteristics, study design, and outcome variables. The methodological quality of each article was assessed using the Quality Assessment with Diverse Studies (QuADS) tool. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed for reporting the results. This study was registered on PROSPERO (#CRD42021245596). SYNTHESIS: Eighty-two studies met inclusion criteria. Findings were qualitatively synthesised into the domains of: antepartum care, intrapartum care, postpartum care, assisted reproductive technologies, abortion access, gynaecological care, sexual health services, and HIV care. Research was concentrated in relatively few countries. Access and delivery were negatively impacted by a variety of factors, including service disruptions, unclear communication regarding policy decisions, decreased timeliness of care, and fear of COVID-19 exposure. Across outpatient services, providers favoured models of care that avoided in-person appointments. Hospitals prioritized models of care that reduced time and number of people in hospital and aerosol-generating environments. CONCLUSIONS: Overall, studies demonstrated reduced access and delivery across most domains of SRH services during COVID-19. Variations in service restrictions and accommodations were heterogeneous within countries and between institutions. Future work should examine long-term impacts of COVID-19, underserved populations, and underrepresented countries.

The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups.

Purpose: To externally test four chest radiograph classifiers on a large, diverse, real-world dataset with robust subgroup analysis. Materials and Methods: In this retrospective study, adult posteroanterior chest radiographs (January 2016-December 2020) and associated radiology reports from Trillium Health Partners in Ontario, Canada, were extracted and de-identified. An open-source natural language processing tool was locally validated and used to generate ground truth labels for the 197 540-image dataset based on the associated radiology report. Four classifiers generated predictions on each chest radiograph. Performance was evaluated using accuracy, positive predictive value, negative predictive value, sensitivity, specificity, F1 score, and Matthews correlation coefficient for the overall dataset and for patient, setting, and pathology subgroups. Results: Classifiers demonstrated 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity on the external testing dataset. Algorithms showed decreased sensitivity for solitary findings (43%-65%), patients younger than 40 years (27%-39%), and patients in the emergency department (38%-60%) and decreased specificity on normal chest radiographs with support devices (59%-85%). Differences in sex and ancestry represented movements along an algorithm's receiver operating characteristic curve. Conclusion: Performance of deep learning chest radiograph classifiers was subject to patient, setting, and pathology factors, demonstrating that subgroup analysis is necessary to inform implementation and monitor ongoing performance to ensure optimal quality, safety, and equity.Keywords: Conventional Radiography, Thorax, Ethics, Supervised Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023See also the commentary by Huisman and Hannink in this issue.

Outcomes in volume replacement and volume displacement techniques in oncoplastic breast conserving surgery: A systematic review.

INTRODUCTION: Volume Replacement (VR-OBCS) and Volume Displacement Oncoplastic Breast Conserving Surgery (VD-OBCS) are commonly used in the management of breast cancer. Many studies summarize the individual postoperative outcomes of these two procedures; however, there is a lack of research that compares outcomes of these approaches. This review summarizes the available VR and VD-OBCS literature in terms of oncological, cosmetic, and clinical outcomes. METHODS: An online literature search (MEDLINE, EMBASE, PubMed, and CINAHL) was performed. Studies were included if they were written in English, had more than 10 adult (18+) female patients who underwent VR-OBCS or VD-OBCS, and reported at least one well-described oncological, clinical, or cosmetic outcome RESULTS: Thirty-three studies (26 VR-OBCS and 7 VD-OBCS) were included in this review; VR-OBCS studies were separated based on the use of latissimus dorsi (LD) flaps. Studies utilizing VR-OBCS with LD flaps reported the highest rate of all oncological outcomes; VR-OBCS studies without LD flaps reported the lowest. Rates of hematoma, seroma, and wound dehiscence were highest in VR-OBCS with LD flaps; partial flap loss and fat necrosis were highest in VR-OBCS without LD flaps and infection was highest in VD-OBCS studies. Inconsistencies in methodology (cosmetic outcome measures, outcome definitions, and time horizons) were found in all procedural groups. CONCLUSION: Differences in outcomes for both OBCS procedures may be due to the heterogeneity of patient populations. "Doers" and "Users" of breast oncoplastic research should consider tumor size, laterality of tumor, breast size, measurement scales, and defensible time horizons before the application of a study's conclusions.