Clinical Scorecard: Evaluation of Tailored GPT Models for Systematic Reviews in Rheumatology: A Two-Part Analysis of Data Extraction and Prognostic Assessment
At a Glance
Category
Detail
Condition
Systematic Reviews in Rheumatology
Key Mechanisms
Customized GPT-based models for data extraction and risk-of-bias appraisal.
Target Population
Researchers and clinicians involved in rheumatology systematic reviews.
Care Setting
Evidence-based decision-making in rheumatology.
Key Highlights
Systematic reviews are essential for evidence-based practice in rheumatology.
Customized GPT models can streamline data extraction and risk-of-bias assessment.
Human reviewers remain critical for ensuring accuracy and consistency.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Potential variability in AI-generated outputs compared to human reviewers.
Patient & Prescribing Data
Not applicable; study involved secondary analysis of literature.
No patient-level data were collected.
Clinical Best Practices
Use of structured templates for data extraction.
Training and consensus among reviewers for QUIPS assessments.
by Pamela Munguía-Realpozo, Edith Ramírez-Lara, Claudia Mendoza-Pinto, Ivet Etchegaray-Morales, Juan Carlos Solis-Poblano, Marco Alejandro Trinidad-González, Jorge Ayón-Aguilar, Máximo Alejandro García-Flores, Álvaro José Montiel-Jarquín
High-revision-rate surgeons saw a late revision signal after adopting robotic-assisted total knee arthroplasty, despite no overall reduction in revision rates or failure modes.
