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PubMed2026

Diagnostic accuracy of bronchoalveolar lavage fluid-based testing for pulmonary cryptococcosis: A systematic review and meta-analysis.

BACKGROUND: Pulmonary cryptococcosis(PC) presents diagnostic challenges because of its non-specific clinical and radiological manifestations. Bronchoalveolar lavage fluid (BALF)-based testing, which includes latex agglutination (LA) and lateral flow assay (LFA), offers a minimally invasive diagnostic method, yet its pooled diagnostic accuracy remains unclear. METHODS: We systematically searched PubMed, Embase, Cochrane Library, and Scopus from inception to May 2026. Studies evaluating BALF-based testing for PC with extractable 2 × 2 data were included. The methodological quality of relevant studies was assessed by the QUADAS-2 tool. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR) were estimated using a bivariate random-effects model. Subgroup analyses were performed by testing method and reference standard type. Heterogeneity was evaluated through paired forest plots, HSROC visualization, and exploratory bivariate meta-regression. RESULTS: The pooled sensitivity was 0.87 (95% CI: 0.81-0.91), and the specificity was 0.99 (95% CI: 0.982 - 0.995). The pooled positive likelihood ratio (PLR) was 88.00 (95% CI: 47.39 - 163.42), the negative likelihood ratio (NLR) was 0.13 (95% CI: 0.09 -0.20), and the DOR was 658.50 (95% CI: 285.36-1519.55). No significant threshold effect or publication bias was detected. Exploratory meta-regression suggested a possible assay-method effect in the joint model (P = 0.03), mainly driven by specificity (P = 0.01). CONCLUSIONS: The study demonstrates the high accuracy of CrAg in BALF for the diagnosis of pulmonary cryptococcosis, supporting its role as an important adjunctive diagnostic tool, particularly when tissue biopsy is not feasible or rapid results are needed. Larger prospective studies with standardized protocols are needed to validate these estimates.

Humans↗
PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗
PubMed2025

Parent-Mediated Interventions for ASD Under 3 Years: A Systematic Review, Meta Analysis, and Moderator Analyses.

This study aimed to assess the effectiveness of PMIs for ASD under 3 years, and explore potential moderators influencing the effectiveness through moderator analyses. The study searched five English databases for randomized controlled trials (RCTs). The meta-analysis was conducted using a random-effects model to calculate Hedges's g. Subgroup analyses and meta regression assessed the effects of potential moderators on PMIs effectiveness, with evidence quality evaluated using GRADE. A total of 31 RCTs were included in the systematic review, with 26 included in the meta-analysis. The results showed a small overall beneficial effect of PMIs on ASD under 3 years (g = 0.20). Small to trivial positive effects were found in several subdomains, including adaptive skills (g = 0.29), parent responsiveness (g = 0.23), parent-child interaction (g = 0.35), social communication (g = 0.18), and symptoms (g =  - 0.22). However, PMIs did not show statistically significant effects on children's cognitive competence, language, or motor skills domains. Subgroup analysis and meta-regression explored potential moderators, but none significantly influenced the effectiveness of PMIs. The GRADE assessment showed that the certainty of the evidence ranged from moderate to low. This study confirmed that PMIs demonstrate positive effects on children under 3 years old with ASD, and showed beneficial outcomes in most subdomains. However, the evidence was of moderate to low certainty, so these findings should be interpreted with caution. In the future, broader databases and more large-scale, multicenter, high-quality clinical studies are needed to confirm these effects.

Humans↗