Guidelines International Network: Principles for Use of Artificial Intelligence in the Health Guideline Enterprise.

Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.

Patient preferences for drug therapy in inflammatory arthritis: protocol for a living systematic review and evidence map to inform clinical practice guidelines.

Introduction: The pharmacological management of inflammatory arthritis often requires choices that involve trade-offs between benefits, risks and other attributes such as administration route, frequency and cost. This living systematic review aims to inform international clinical guidelines on inflammatory arthritis by creating an evidence map of patient preference studies concerning the trade-offs in pharmacological management of inflammatory arthritis.

Methods And Analysis: We will include published and peer-reviewed full-text studies in any language that quantitatively assess preferences of patients for the pharmacological management of inflammatory arthritis (rheumatoid arthritis, spondyloarthritis and juvenile idiopathic arthritis).

Development of a framework to structure decision-making in environmental and occupational health: A systematic review and Delphi study.

Background: Environmental and occupational health (EOH) assessments increasingly utilize systematic review methods and structured frameworks for evaluating evidence about the human health effects of exposures. However, there is no prevailing approach for how to integrate this evidence into decisions or recommendations. Grading of Recommendations Assessment, Development and Evaluation (GRADE) evidence-to-decision (EtD) frameworks provide a structure to support standardized and transparent consideration of relevant criteria to inform health decisions.

Empirical estimation of disutilities and decision thresholds for composite endpoints.

Objectives: The evaluation of health benefits and harms of an intervention with GRADE Evidence to Decision (EtD) frameworks includes judgments if the effects are "trivial," "small," "moderate," or "large." Such judgments ideally require the a priori establishment of decision thresholds (DTs), whose empirical derivation for single outcomes has been previously described. In this article, we provide a methodological approach to estimate DTs for composite endpoints based on disutilities.