Efficiency and workload reduction of citation screening using a large language model: a retrospective observational study
Not Applicable
Not yet recruiting
- Conditions
- Sepsis
- Registration Number
- JPRN-UMIN000053091
- Lead Sponsor
- Other
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- All
- Target Recruitment
- 2
Inclusion Criteria
Not provided
Exclusion Criteria
Not applicable
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method
- Secondary Outcome Measures
Name Time Method
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What molecular mechanisms underlie sepsis progression and how might large language models identify novel therapeutic targets for sepsis management?
How does large language model-assisted citation screening compare to traditional methods in terms of accuracy and efficiency for sepsis-related research?
Are there specific biomarkers in sepsis patients that can be leveraged to optimize citation screening workflows using AI technologies?
What adverse events are commonly associated with current sepsis treatments and how might AI-driven approaches improve risk prediction and management?
What combination therapies or competitor drugs show promise in sepsis management and how does this trial's AI methodology fit within the broader therapeutic landscape?