Effectiveness of The Umbrella Collaboration Versus Traditional Umbrella Reviews for Evidence Synthesis in Health Care: Protocol for a Validation Study
Bibliographic record
Abstract
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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/67248.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.289 | 0.374 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".