“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...

Although Africa is a region of hyper endemicity to viral hepatitis B (HBV) and C (HCV) infections, there is limited data on their related burden among pregnant women. The present systematic review and meta-analysis aimed to determine the magnitude of these infections among pregnant women living in Africa and investigate its association with gender-related human development indicators. We searched PubMed, Embase, Web of Science, Africa Journal Online, and Global Index Medicus, with no language restriction, to identify observational studies on HBV and HCV infections in pregnant women residing in Africa published from January 1, 2000 until December 31, 2017. Eligible studies reported the prevalence of HBV and/or HCV infection(s) (HBs antigen and HCV antibodies) and/or infectivity (HBe antigen or detectable HCV viral load). Each study was independently reviewed for methodological quality. We used a random-effects model meta-analysis to pool studies. In total, 145 studies (258 251 participants, 30 countries) were included, of which 120 (82.8%) had a low, 24 (16.5%) a moderate, and one (0.7%) had a high risk of bias. The prevalence of HBV and HCV infections was 6.8% (95% confidence interval [CI]: 6.1–7.6, 113 studies) and 3.4% (95% CI: 2.6–4.2, 58 studies), respectively. The prevalence of HBe antigen and HCV detectable viral load was 18.9% (95% CI: 14.4–23.9) and 62.3% (95% CI: 51.6–72.5) in HBV positive and HCV positive pregnant women, respectively. The multivariable meta-regression analysis showed that the prevalence of HBV infection increased with decreasing gender development index, males’ level of education and females’ expected years of schooling. Furthermore, this prevalence was higher in rural areas and in western and central Africa. The prevalence of HCV infection increased with decreasing proportion of seats held by women in parliament. To address the burden of HBV and HCV infections, beyond well-known risk factors at the individual-level, macro-level factors including gender-related human development indicators and dwelling in rural areas should be considered. In Africa, HBV or HCV infected mothers seems to have high potential of transmission to their children.

The purpose of this study was to compare the ability of two multimodal models (GPT-4o and Gemini 1.5 Pro) with that of radiologists to generate differential diagnoses from textual context alone, key images alone, or a combination of both using complex neuroradiology cases. This retrospective study included neuroradiology cases from the "Diagnosis Please" series published in the Radiology journal between January 2008 and September 2024. The two multimodal models were asked to provide three differential diagnoses from textual context alone, key images alone, or the complete case. Six board-certified neuroradiologists solved the cases in the same setting, randomly assigned to two groups: context alone first and images alone first. Three radiologists solved the cases without, and then with the assistance of Gemini 1.5 Pro. An independent radiologist evaluated the quality of the image descriptions provided by GPT-4o and Gemini for each case. Differences in correct answers between multimodal models and radiologists were analyzed using McNemar test. GPT-4o and Gemini 1.5 Pro outperformed radiologists using clinical context alone (mean accuracy, 34.0 % [18/53] and 44.7 % [23.7/53] vs. 16.4 % [8.7/53]; both P < 0.01). Radiologists outperformed GPT-4o and Gemini 1.5 Pro using images alone (mean accuracy, 42.0 % [22.3/53] vs. 3.8 % [2/53], and 7.5 % [4/53]; both P < 0.01) and the complete cases (48.0 % [25.6/53] vs. 34.0 % [18/53], and 38.7 % [20.3/53]; both P < 0.001). While radiologists improved their accuracy when combining multimodal information (from 42.1 % [22.3/53] for images alone to 50.3 % [26.7/53] for complete cases; P < 0.01), GPT-4o and Gemini 1.5 Pro did not benefit from the multimodal context (from 34.0 % [18/53] for text alone to 35.2 % [18.7/53] for complete cases for GPT-4o; P = 0.48, and from 44.7 % [23.7/53] to 42.8 % [22.7/53] for Gemini 1.5 Pro; P = 0.54). Radiologists benefited significantly from the suggestion of Gemini 1.5 Pro, increasing their accuracy from 47.2 % [25/53] to 56.0 % [27/53] (P < 0.01). Both GPT-4o and Gemini 1.5 Pro correctly identified the imaging modality in 53/53 (100 %) and 51/53 (96.2 %) cases, respectively, but frequently failed to identify key imaging findings (43/53 cases [81.1 %] with incorrect identification of key imaging findings for GPT-4o and 50/53 [94.3 %] for Gemini 1.5). Radiologists show a specific ability to benefit from the integration of textual and visual information, whereas multimodal models mostly rely on the clinical context to suggest diagnoses.

Background: The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically. Objective: The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs). Methods: SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values. Results: In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8% (62.1%-95.2%) and 71.4% (42.6%-90%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67). Conclusions: Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs.