Background: The synthesis of evidence in health care is essential for informed decision-making and policy development. This study aims to validate The Umbrella Collaboration (TU), an innovative, semiautomatic tertiary evidence synthesis methodology, by comparing it with Traditional Umbrella Reviews (TUR), which are currently the gold standard. Objective: This study aimed to evaluate whether TU, an artificial intelligence—assisted, software-driven system for tertiary evidence synthesis, can achieve comparable effectiveness to TURs, while offering a more timely, efficient, and comprehensive approach. In addition, as a secondary objective, the study aims to assess the accessibility and comprehensibility of TU’s outputs to ensure its usability and practical applicability for health care professionals. Methods: This protocol outlines a comparative study divided into 2 main parts. The first part involves a quantitative comparison of results obtained using TU and TURs in geriatrics. We will evaluate the identification, size effect, direction, statistical significance, and certainty of outcomes, as well as the time and resources required for each methodology. Data for TURs will be sourced from Medline (via PubMed), while TU will use artificial intelligence—assisted informatics to replicate the research questions of the selected TURs. The second part of the study assesses the ease of use and comprehension of TU through an online survey directed at health professionals, using interactive features and detailed data access. Results: Expected results include the assessment of concordance in identifying outcomes, the size effect, direction and significance of these outcomes, and the certainty of evidence. In addition, we will measure the operational efficiency of each methodology by evaluating the time taken to complete projects. User perceptions of the ease of use and comprehension of TU will be gathered through detailed surveys. The implementation of new methodologies in evidence synthesis requires validation. This study will determine whether TU can match the accuracy and comprehensiveness of TURs while offering benefits in terms of efficiency and user accessibility. The comparative study is designed to address the inherent challenges in validating a new methodology against established standards. Conclusions: If TU proves as effective as TURs but more time-efficient, accessible, and easily updatable, it could significantly enhance the process of evidence synthesis, facilitating informed decision-making and improving health care. This study represents a step toward integrating innovative technologies into routine evidence synthesis practice, potentially transforming health research.
Background: The synthesis of evidence in healthcare is essential for informed decision-making and policy development. This study aims to validate The Umbrella Collaboration (TU), an innovative, semi-automatic tertiary evidence synthesis methodology, by comparing it with Traditional Umbrella Reviews (TUR), which are currently the gold standard. Objective: The primary objective of this study is to evaluate whether TU, an AI-assisted, software-driven system for tertiary evidence synthesis, can achieve comparable effectiveness to TURs, while offering a more timely, efficient, and comprehensive approach. Methods: This comparative study evaluated TU against TURs across eight matched projects in geriatrics. For each selected TUR, a parallel TU project was conducted using the same research question. Outcomes of interest (OoIs), effect sizes, certainty ratings, and execution times were systematically compared. Effect sizes were assessed both quantitatively, by transforming TUR metrics to Cohen’s d and correlating them with TU’s RTU metric, and qualitatively, through categorical classifications (trivial, small, moderate, large). Certainty levels were compared by mapping GRADE ratings and TU’s sentiment analysis scores onto a common 0–1 scale. Execution time was measured precisely in TU, while TUR durations were estimated from literature benchmarks. Statistical analyses included chi-squared tests and Spearman correlations. Results: Eight TURs in geriatrics were matched with parallel projects using TU. TU replicated 84.9% (73/86) of the OoIs identified by TURs and reported an additional 337 OoIs, representing a 4.77-fold increase in outcome identification. In the comparison of effect size classifications, full concordance was observed in 50.0% of cases and consistent concordance (full plus one-level deviation) in 93.8%, with a moderate strength of association (Cramér’s V = 0.339). The correlation of transformed certainty values between TU and GRADE yielded a statistically significant Spearman coefficient (ρ = 0.446; P = .025). The average execution time per TU project was 4 hours and 46 minutes, compared to estimated durations of 6–12 months for TURs. Conclusions: The Umbrella Collaboration demonstrated high concordance with TURs, replicating 84.9% of the outcomes identified by TURs and identifying nearly five times as many additional outcomes. The experimental effect size metric (RTU) showed moderate agreement with conventional measures, and the certainty ratings derived from sentiment analysis correlated acceptably with GRADE-based assessments. While further validation is needed, TU appears to be a valid and efficient approach for tertiary evidence synthesis, offering a scalable and time-efficient alternative when rapid results are required.