Can large language models fully automate or partially assist paper selection in systematic reviews?

Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.

Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).

Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods.

Developing high-performance alloys is essential for applications in advanced electromagnetic energy conversion devices. In this study, we assess Fe-Co-Ni alloy compositions identified in our previous work through a machine learning (ML) framework, which used both multi-property ML models and multi-objective Bayesian optimization to design compositions with predicted high values of saturation magnetization, Curie temperature, and Vickers hardness. Experimental validation was conducted on two promising compositions synthesized using three different methods: arc melting, ball milling followed by spark plasma sintering (SPS), and chemical synthesis followed by SPS.

Prevalence and predictors of positive childhood experiences and their relationship with adverse childhood experiences among young adults in Delhi-NCR, India.

Background: Although positive childhood experiences (PCEs) have been reported to be crucial for healthy development and better mental and physical health outcomes, their epidemiology and relationship with adverse childhood experiences (ACE) exposure in low and middle-income countries, including India remain underexplored.

Aims: The present study aimed to ascertain the prevalence and predictors of PCE exposure and understand the relationship between PCE and ACE exposure among young adults in Delhi-NCR, India.

Methods: The present cross-sectional study involved a total of 1,573 young adults (18-25 years) of both sexes (69.

Serum Levels of Heavy Metals in Patients with Prostate Cancer: A Systematic Review and Meta-analysis.

Prostate cancer (PC) is a common malignancy among men globally. Although genetic, hormonal, and environmental factors contribute to its development, the role of heavy metals remains unclear. This study evaluated serum levels of arsenic, cadmium, lead, mercury, and nickel in PC patients compared to healthy controls.

Community Synchrony in Seed Production is Associated With Trait Similarity and Climate Across North America.

Mast seeding, the synchronous and highly variable production of seed crops by perennial plants, is a population-level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long-term data are rarely available.