{ "generated_at": "2026-04-25T11:28:34.409870+08:00", "timezone": "Asia/Shanghai", "lookback_hours": 24, "sorting": { "default_sort_by": "hybrid", "summary": "hybrid (relevance first, published_at tie-break)", "weights": { "title_match_weight": 40, "summary_match_weight": 18, "doi_weight": 12, "pdf_weight": 8, "rich_summary_weight": 6, "metadata_weight": 4, "multi_source_weight": 10, "freshness_weight_cap": 24 }, "feeds": [ { "name": "LLM", "sort_by": "hybrid" }, { "name": "Vision", "sort_by": "hybrid" }, { "name": "PubMed AI", "sort_by": "hybrid" }, { "name": "OpenAlex AI", "sort_by": "hybrid" } ] }, "highlights": [ "主题「Clinical」：命中 4 篇，覆盖 PubMed AI，代表论文包括 《Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.》、《Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.》。", "主题「Language Model」：命中 3 篇，覆盖 PubMed AI，代表论文包括 《Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.》、《Considerations about the proliferation of large language model chatbots and youth mental health.》。", "主题「Benchmark」：命中 2 篇，覆盖 PubMed AI，代表论文包括 《Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.》、《Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance.》。" ], "focus_items": [], "action_items": [ { "canonical_id": "arxiv:2604.06170", "title": "Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework", "abstract_url": "https://arxiv.org/abs/2604.06170v1", "summary": "The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes such as concepts, methods, experiments, and figures, enabling graph-aware question answering and coverage verification. Both pipelines are implemented within a coder LLM-based multi-agent orchestration framework and produce fully reproducible, synchronized outputs including JSON, CSV, BibTeX, Markdown, and HTML at each agent step. This paper describes the system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces that together form the Paper Circle research workflow. We benchmark Paper Circle on both paper retrieval and paper review generation, reporting hit rate, MRR, and Recall at K. Results show consistent improvements with stronger agent models. We have publicly released the website at https://papercircle.vercel.app/ and the code at https://github.com/MAXNORM8650/papercircle.", "source_label": "arxiv", "feedback_status": "star", "feedback_note": "Anchor paper for the multi-agent discovery workflow; compare its planner design with newer agent benchmarks.", "next_action": "compare planner design with newer agent benchmarks", "due_date": "2026-04-18", "days_until_due": -7, "review_interval_days": null, "reasons": [ "overdue", "overdue_7d", "next_action_pending" ], "feed_names": [ "LLM" ], "relevance_score": 0, "active_days": 1, "active_feeds": 1, "appearance_count": 1, "first_seen": "2026-04-08T17:10:24.386535+08:00", "last_seen": "2026-04-08T17:10:24.386535+08:00" } ], "topic_sections": [ { "name": "Clinical", "paper_count": 4, "feed_names": [ "PubMed AI" ], "paper_titles": [ "Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.", "Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.", "Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance.", "Considerations about the proliferation of large language model chatbots and youth mental health." ], "key_points": [ "《Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.》〔评测 / 方法〕：This study aimed to establish benchmarks for clinically significant change for the Moral Injury Outcome Scale (MIOS) using national data from Veterans treated…", "《Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.》〔评测 / 数据 / 应用 / 方法〕：Creating language-specific and domain-specific large language models presents substantial challenges, including computational demands and limited data availabi…" ] }, { "name": "Language Model", "paper_count": 3, "feed_names": [ "PubMed AI" ], "paper_titles": [ "Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.", "Considerations about the proliferation of large language model chatbots and youth mental health.", "The application of large language models in meteorology graduate research: current status, impact, and prospects." ], "key_points": [ "《Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.》〔评测 / 数据 / 应用 / 方法〕：Creating language-specific and domain-specific large language models presents substantial challenges, including computational demands and limited data availabi…", "《Considerations about the proliferation of large language model chatbots and youth mental health.》〔应用 / 方法〕：Young people are experiencing worsening mental health and a growing reliance on online tools and services to address mental health difficulties. At the same ti…" ] }, { "name": "Benchmark", "paper_count": 2, "feed_names": [ "PubMed AI" ], "paper_titles": [ "Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.", "Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance." ], "key_points": [ "《Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.》〔评测 / 方法〕：This study aimed to establish benchmarks for clinically significant change for the Moral Injury Outcome Scale (MIOS) using national data from Veterans treated…", "《Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance.》〔评测 / 方法〕：BACKGROUND: Subject identification is a cornerstone of data integrity and regulatory compliance in clinical trials. Legacy, study-specific subject ID conventio…" ] } ], "template": "zh_daily_brief", "feeds": [ { "name": "LLM", "key_points": [], "sort_by": "hybrid", "papers": [] }, { "name": "Vision", "key_points": [], "sort_by": "hybrid", "papers": [] }, { "name": "PubMed AI", "key_points": [ "《Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.》〔评测 / 方法〕：This study aimed to establish benchmarks for clinically significant change for the Moral Injury Outcome Scale (MIOS) using national data from Veterans treated…", "《Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.》〔评测 / 数据 / 应用 / 方法〕：Creating language-specific and domain-specific large language models presents substantial challenges, including computational demands and limited data availabi…", "《Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance.》〔评测 / 方法〕：BACKGROUND: Subject identification is a cornerstone of data integrity and regulatory compliance in clinical trials. Legacy, study-specific subject ID conventio…" ], "sort_by": "hybrid", "papers": [ { "title": "Establishing Clinically Significant Change Benchmarks for the Moral Injury Outcome Scale in VA Behavioral Health Settings.", "summary": "This study aimed to establish benchmarks for clinically significant change for the Moral Injury Outcome Scale (MIOS) using national data from Veterans treated in U.S. Department of Veterans Affairs (VA) behavioral health settings. We analyzed archival electronic health record data from 2,521 Veterans administered the MIOS between July 2022 and March 2025. A subset of 361 Veterans who completed at least two MIOS administrations within 4 months constituted the episode-of-care cohort. Reliable change indices (RCIs) and functional recovery thresholds were calculated using the Jacobson and Truax method. A change score of 13 points on the MIOS indicated clinically significant improvement and the critical value suggesting functional recovery for endpoint scores was ≤9. Most Veterans were unchanged (81%), with 11.9% showing reliable improvement, 4.2% probable recovery, and 2.8% deterioration. In the larger cohort, nearly half met the criterion for probable moral injury. MIOS administration was most common in general mental health and post-traumatic stress disorder (PTSD) specialty care clinics. These initial findings provide the first clinically significant change benchmarks for the MIOS, supporting its integration into measurement-based care and routine outcome monitoring for moral injury in Veterans.", "authors": [ "Brett T Litz", "Hannah E Walker", "Luke Rusowicz-Orazem", "Zoe R Styler", "Elliot Fielstein", "Benjamin Darnell", "Keith G Meador", "Jason A Nieuwsma" ], "categories": [ "Journal Article" ], "paper_id": "pubmed:42027113", "abstract_url": "https://pubmed.ncbi.nlm.nih.gov/42027113/", "pdf_url": null, "published_at": "2026-04-24T04:12:00+00:00", "updated_at": "2026-04-24T04:12:00+00:00", "source": "pubmed", "date_label": "Entered", "analysis": null, "tags": [ "评测", "方法" ], "topics": [ "Clinical", "Benchmark" ], "doi": "10.1177/10731911261436687", "arxiv_id": null, "source_variants": [ "pubmed" ], "source_urls": { "pubmed": "https://pubmed.ncbi.nlm.nih.gov/42027113/", "doi": "https://doi.org/10.1177/10731911261436687" }, "relevance_score": 102, "match_reasons": [ "title matched \"benchmark\"", "title matched \"clinical\"", "has DOI", "has rich summary", "has complete metadata" ], "feedback_status": null, "feedback_note": null, "feedback_next_action": null, "feedback_due_date": null, "feedback_snoozed_until": null, "feedback_review_interval_days": null, "canonical_id": "doi:10.1177/10731911261436687" }, { "title": "Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.", "summary": "Creating language-specific and domain-specific large language models presents substantial challenges, including computational demands and limited data availability. While it is commonly believed that the benefits of specialized models justify these challenges, we dispute this notion with a comparative evaluation in a low-resourced language and medical-specific domain. In our study, we analyze the performance of various LLMs applied to the Italian healthcare domain using novel unpublished datasets, consisting of five-choice questions from national pre-university and post-university medical exams, covering clinical and preclinical fields. As part of this work, we release these datasets to the research community. We evaluated multilingual and Italian-specific models, along with general-purpose and healthcare-specific models, spanning both open-source and proprietary architectures of varying sizes. Our results demonstrate that multilingual, general-purpose large models consistently exceed the pass threshold across all tests, with the best models achieving over 90% accuracy on postgraduate-level exams. Model size emerged as the most critical factor influencing performance, whereas domain specialization and single-language localization offered no evidence of specialization superiority. These findings challenge the traditional pretrain-then-finetune paradigm for domain and language localization in language models, suggesting that advancements in generic-purpose multilingual models may render domain-specific pretraining unnecessary in many specialized cases.", "authors": [ "Tommaso Mario Buonocore", "Antonio Russo", "Dario Mingarelli", "Enea Parimbelli" ], "categories": [ "Journal Article" ], "paper_id": "pubmed:42030281", "abstract_url": "https://pubmed.ncbi.nlm.nih.gov/42030281/", "pdf_url": null, "published_at": "2026-04-24T13:43:00+00:00", "updated_at": "2026-04-24T13:43:00+00:00", "source": "pubmed", "date_label": "Entered", "analysis": null, "tags": [ "评测", "数据", "应用", "方法" ], "topics": [ "Clinical", "Language Model" ], "doi": "10.1371/journal.pdig.0001363", "arxiv_id": null, "source_variants": [ "pubmed" ], "source_urls": { "pubmed": "https://pubmed.ncbi.nlm.nih.gov/42030281/", "doi": "https://doi.org/10.1371/journal.pdig.0001363" }, "relevance_score": 90, "match_reasons": [ "title matched \"language model\"", "summary matched \"clinical\"", "has DOI", "has rich summary", "has complete metadata" ], "feedback_status": null, "feedback_note": null, "feedback_next_action": null, "feedback_due_date": null, "feedback_snoozed_until": null, "feedback_review_interval_days": null, "canonical_id": "doi:10.1371/journal.pdig.0001363" }, { "title": "Standardization of clinical trials subject ID schematics: A portfolio-wide model to enhance data integrity and regulatory compliance.", "summary": "BACKGROUND: Subject identification is a cornerstone of data integrity and regulatory compliance in clinical trials. Legacy, study-specific subject ID conventions may cause risk of duplication, hinder traceability of rescreened participants, and complicate regulatory submissions-particularly in large global portfolios where multiple trials for similar disease areas from the same sponsor are handled by the same site and the same PI. Regulatory guidance from the U.S. Food and Drug Administration (FDA Technical Conformance Guide), the Clinical Data Interchange Standards Consortium (CDISC SDTM), and ICH E6(R2) mandates unique subject traceability throughout a study's lifecycle. AIM: This paper introduces, validates, and evaluates a standardized subject-identification schema designed to eliminate risk of duplication, ensure traceable rescreening, and harmonize subject IDs across an organization's clinical portfolio while aligning with global regulatory requirements. METHODS: A cross-functional Biogen team spanning Global Clinical Operations, Data Systems, IT, Clinical Supply, with inputs from external partners (CROs and IXRT vendors) designed a new schema (SSSS-PYZ-XXXA). The structure encodes site (SSSS), program (P), phase (Y), study sequence (Z), subject number (XXX), and screening attempt (A). Validation comprised retrospective pressure testing with historical data, pilot implementation in active trials, and benchmarking against CDISC SDTM and FDA TCG standards. RESULTS: The standardized schema eliminated subject-ID overlap across parallel studies, enabled seamless rescreen tracking without creating multiple USUBJIDs, and proved compatible with EDC, CTMS, IXRT, and LIMS systems. As of 2025, the schema had been adopted in at least 59 new trials across multiple therapeutic areas, improving SDTM mapping and regulatory preparedness. CONCLUSION: A portfolio-wide standardized subject-ID schema provides a sustainable, scalable framework that strengthens data integrity, streamlines operations, and enhances regulatory compliance across clinical development programs.", "authors": [ "Ananya Jain", "Steve Demas", "Todd Bazin" ], "categories": [ "Journal Article" ], "paper_id": "pubmed:42028262", "abstract_url": "https://pubmed.ncbi.nlm.nih.gov/42028262/", "pdf_url": null, "published_at": "2026-04-24T04:56:00+00:00", "updated_at": "2026-04-24T04:56:00+00:00", "source": "pubmed", "date_label": "Entered", "analysis": null, "tags": [ "评测", "方法" ], "topics": [ "Clinical", "Benchmark" ], "doi": "10.1016/j.conctc.2026.101641", "arxiv_id": null, "source_variants": [ "pubmed" ], "source_urls": { "pubmed": "https://pubmed.ncbi.nlm.nih.gov/42028262/", "doi": "https://doi.org/10.1016/j.conctc.2026.101641" }, "relevance_score": 81, "match_reasons": [ "title matched \"clinical\"", "summary matched \"benchmark\"", "has DOI", "has rich summary", "has complete metadata" ], "feedback_status": null, "feedback_note": null, "feedback_next_action": null, "feedback_due_date": null, "feedback_snoozed_until": null, "feedback_review_interval_days": null, "canonical_id": "doi:10.1016/j.conctc.2026.101641" }, { "title": "Considerations about the proliferation of large language model chatbots and youth mental health.", "summary": "Young people are experiencing worsening mental health and a growing reliance on online tools and services to address mental health difficulties. At the same time, next-generation large language models (LLMs) that are deployed through 'chatbot style interfaces', using deep learning artificial intelligence akin to interacting with a human appear to mark an opportunity for mental health therapeutics when designed specifically for clinical intervention. However, emergent evidence suggests the use of more generic LLM chatbots may pose a risk of providing misinformation, bias, or over reliance for some individuals when used outside of clinical contexts for mental health. This perspective paper examines the intersection of youth mental health and the rapid adoption of LLM chatbots. It first contextualises rising mental health challenges among young people alongside their increasing reliance on digital solutions. The paper then explores the potential benefits of LLM chatbot style interfaces in clinical mental health interventions. Following this, we discuss the evidence surrounding adverse mental health outcomes from the use of generic LLMs to support mental health at population level, describing complex system-level and human-level factors noted from the evidence. Finally, we outline considerations for public health and youth mental health discourse, purpose built LLM platform design, and a supporting research agenda. While current evidence on benefits and risks from generic LLMs is emergent and not youth-specific, this perspective highlights a need for research focused on young people to ensure safe and effective use of widely available LLMs for mental health support.", "authors": [ "Evan Matthews", "Frances Cleary", "Joseph Firth" ], "categories": [ "Journal Article" ], "paper_id": "pubmed:42027103", "abstract_url": "https://pubmed.ncbi.nlm.nih.gov/42027103/", "pdf_url": null, "published_at": "2026-04-24T04:03:00+00:00", "updated_at": "2026-04-24T04:03:00+00:00", "source": "pubmed", "date_label": "Entered", "analysis": null, "tags": [ "应用", "方法" ], "topics": [ "Clinical", "Language Model" ], "doi": "10.1017/ipm.2026.10195", "arxiv_id": null, "source_variants": [ "pubmed" ], "source_urls": { "pubmed": "https://pubmed.ncbi.nlm.nih.gov/42027103/", "doi": "https://doi.org/10.1017/ipm.2026.10195" }, "relevance_score": 80, "match_reasons": [ "title matched \"language model\"", "summary matched \"clinical\"", "has DOI", "has rich summary", "has complete metadata" ], "feedback_status": null, "feedback_note": null, "feedback_next_action": null, "feedback_due_date": null, "feedback_snoozed_until": null, "feedback_review_interval_days": null, "canonical_id": "doi:10.1017/ipm.2026.10195" }, { "title": "The application of large language models in meteorology graduate research: current status, impact, and prospects.", "summary": "With the rapid development of generative artificial intelligence, large language models (LLMs) have gradually integrated into various fields, demonstrating significant potential, particularly in meteorological research. This study explores the current application, advantages, challenges, and future development trends of LLMs in the scientific work of meteorology graduate students at NUIST. Through surveys and case analysis, the study finds that LLMs are primarily applied in literature review, data processing, code development, and academic writing in meteorological research. The results show that LLMs significantly enhance research efficiency, particularly in code development and literature translation, saving considerable time for graduate students. However, challenges remain in areas such as the accuracy of professional knowledge, creative inspiration, and interdisciplinary integration. The study also reveals concerns over data security, academic integrity, and model limitations when using LLMs. Future applications of LLMs in meteorology need further optimization in terms of professional knowledge accuracy and data processing capabilities. This paper provides both theoretical support and practical guidance for the responsible integration of LLMs into meteorological research and education.", "authors": [ "Siguang Zhu", "Honghui Li" ], "categories": [ "Journal Article" ], "paper_id": "pubmed:42030357", "abstract_url": "https://pubmed.ncbi.nlm.nih.gov/42030357/", "pdf_url": null, "published_at": "2026-04-24T13:54:00+00:00", "updated_at": "2026-04-24T13:54:00+00:00", "source": "pubmed", "date_label": "Entered", "analysis": null, "tags": [ "应用", "方法" ], "topics": [ "Language Model" ], "doi": "10.1371/journal.pone.0347933", "arxiv_id": null, "source_variants": [ "pubmed" ], "source_urls": { "pubmed": "https://pubmed.ncbi.nlm.nih.gov/42030357/", "doi": "https://doi.org/10.1371/journal.pone.0347933" }, "relevance_score": 72, "match_reasons": [ "title matched \"language model\"", "has DOI", "has rich summary", "has complete metadata" ], "feedback_status": null, "feedback_note": null, "feedback_next_action": null, "feedback_due_date": null, "feedback_snoozed_until": null, "feedback_review_interval_days": null, "canonical_id": "doi:10.1371/journal.pone.0347933" } ] }, { "name": "OpenAlex AI", "key_points": [], "sort_by": "hybrid", "papers": [] } ] }