Evidence map›Paper›PMID 40627806›Full record

ArticleJMIR formative research2025

Validation of The Umbrella Collaboration for Tertiary Evidence Synthesis in Geriatrics: Mixed Methods Study.

Beltran Carrillo, Marta Rubinos-Cuadrado, Jazmin Parellada, Alejandra Palacios, Beltran Carrillo-Rubinos, Fernando Canillas, Juan José Baztán Cortés, Javier Gómez-Pavón

Abstract readValidation Study
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Beltran Carrillo *The Umbrella Collaboration, C/ Ferraz, 49, Madrid, 28008, Spain, 34 637016776.ORCID 0000-0001-5748-9423
Marta Rubinos-Cuadrado *The Umbrella Collaboration, C/ Ferraz, 49, Madrid, 28008, Spain, 34 637016776.ORCID 0000-0003-0108-2629
Jazmin ParelladaThe Umbrella Collaboration, C/ Ferraz, 49, Madrid, 28008, Spain, 34 637016776.ORCID 0000-0002-8528-4481
Alejandra PalaciosThe Umbrella Collaboration, C/ Ferraz, 49, Madrid, 28008, Spain, 34 637016776.ORCID 0000-0002-7263-9167
Beltran Carrillo-RubinosThe Umbrella Collaboration, C/ Ferraz, 49, Madrid, 28008, Spain, 34 637016776.ORCID 0000-0002-0796-4414
Fernando CanillasDepartment of Traumatology and Orthopedic Surgery, Hospital Central de la Cruz Roja San José y Santa Adela, Madrid, Spain.ORCID 0000-0002-3326-3494
Juan José Baztán CortésDepartment of Geriatrics, Hospital Central de la Cruz Roja San José y Santa Adela, Madrid, Spain.ORCID 0009-0009-9792-0404
Javier Gómez-PavónDepartment of Geriatrics, Hospital Central de la Cruz Roja San José y Santa Adela, Madrid, Spain.ORCID 0000-0002-4732-6265

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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, semiautomated tertiary evidence synthesis methodology, by comparing it with traditional umbrella reviews (TURs), which are currently the gold standard. Objective: The primary objective of this study is to evaluate whether TU, an artificial intelligence-assisted, software-driven system for tertiary evidence synthesis, can achieve effectiveness comparable to that of TURs, while offering a more timely, efficient, and comprehensive approach. Methods: This comparative study evaluated TU against TURs across 8 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 d and correlating them with TU's RTU metric, and qualitatively, through categorical classifications (trivial, small, moderate, and large). Certainty levels were compared by mapping Grading of Recommendations Assessment, Development, and Evaluation (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-square tests and Spearman correlations. Results: Eight TURs in geriatrics were matched with parallel projects using TU. TU replicated 73 of the 86 (85%) 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 24 of the 48 (50%) cases, and consistent concordance (full plus 1-level deviation) in 45 of the 48 (94%) cases, with a moderate strength of association (Cramér V=0.339). The correlation of transformed certainty values between TU and GRADE yielded a statistically significant Spearman coefficient (ρ=0.446; P=.02). The average execution time per TU project was 4 hours and 46 minutes, compared with estimated durations of 6-12 months for TURs. Conclusions: The TU demonstrated high concordance with TURs, replicating 73 of the 86 (85%) outcomes identified by TURs and identifying nearly 5 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.

Indexed as

Evidence-Based MedicineGeriatricsArtificial IntelligenceHumansReproducibility of ResultsResearch DesignAI-assisted synthesisalgorithmsanalyticsartificial intelligencedigital healthdigital interventionsdigital technologyevidence-based decision-makinghealth research methodologymachine learningmodelstertiary evidence synthesisThe Umbrella Collaborationumbrella reviews

Identifiers

PMID40627806
PMCPMC12262930

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.