Evidence map›Paper›PMID 41005357›Full record

ArticleApplied clinical informatics2025

Best Practices to Design, Plan, and Execute Large-Scale Federated Analyses-Key Learnings and Suggestions from a Study Comprising 52 Databases.

Theresa Burkard, Montse Camprubi, Daniel Prieto-Alhambra, Peter Rijnbeek, Marta Pineda-Moncusi

Abstract read
In one paragraph

Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Theresa BurkardNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
Montse CamprubiSynapse, Carrer de la Diputació, Barcelona, Cataluña, Spain.
Daniel Prieto-AlhambraNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
Peter RijnbeekDepartment of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Marta Pineda-MoncusiNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Federated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated analyses using the OMOP CDM within the European Health Data and Evidence Network (EHDEN) as an essential tool for generating large-scale evidence.We conducted large-scale federated analyses involving 52 databases from 19 countries using the OMOP CDM. In this

Indexed as

Databases, FactualFederated Learning

Identifiers

PMID41005357
PMCPMC12575070

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.