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Record W7084194277

Expert Perspectives on Next Generation Health Guidelines: How to Integrate RWE in EBM

2025· article· en· W7084194277 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture, Water, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthHistory of medicineColumbia universityHealth careResearch centre
DOInot available

Abstract

fetched live from OpenAlex

Stefano Del Giacco,1,* Giorgio Walter Canonica,2,* Ioana Agache,3 David Price,4,5 Nicolas Roche,6 Holger Schunemann,7,8 Keith Allan,9 Ignacio Ansotegui,10 Simona Barbaglia,11 Jonathan A Bernstein,12 Matteo Bonini,13,14 Sinthia Bosnic-Anticevich,15,16 Jean Bousquet,17,18 Fulvio Braido,19,20 Victoria Carter,21 Herberto Jose Chong-Neto,22 Danilo Di Bona,23 Kirsty Fletton,5 Sandra Gonzalez Diaz,24 Vandana Ayyar Gupta,25 Richard Hubbard,26 Jonathan Iaccarino,27 Ibon Eguiluz-Gracia,28 Cristina Jacomelli,5 Janwillem Kocks,4,29– 31 Jerry Krishnan,3 Vera Mahler,32 Mario Morais-Almeida,33 Daniel Moreles,34 Paola Muti,35,36 Susanna Palkonen,37 Nikolaos G Papadopoulos,38,39 Ruby Pawankar,40 Christina Reeb,32 Helen Reddel,41 Isabel Rojo,42 Dermot Ryan,43 Lydia Sodhi,4 Maria Torres,28,44 Tonya Winders,45 Kevin C Wilson46 1Department of Medical Sciences and Public Health, University of Cagliari, Monserrato, Cagliari, Italy; 2Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Hospital IRCCS, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; 3Faculty of Medicine, Transylvania University, Brasov, Romania; 4Observational and Pragmatic Research Institute, Singapore; 5Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK; 6Respiratory Medicine Department, Hôpitaux Universitaires Paris Centre, Hôpital Cochin, AP-HP and Université Paris Cité, Paris, France; 7Department of Health Research Methods, Evidence, and Impact, and of Medicine, McMaster University, Hamilton, ON, Canada; 8McMaster GRADE Centre & Department of Biomedical Sciences, Humanitas University, Milan, Italy; 9Department of Patient and Community Engagement, University Hospitals of Leicester, Leicester, UK; 10Department of Allergy and Immunology, Hospital Quironsalud Bizkaia, Bilbao, Spain; 11Leadership Team, Respiriamo Insieme Association, Padova, Italy; 12Division of Immunology/Allergy Section, Department of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA; 13Department of Public Health and Infectious Diseases, Sapienza University, Rome, Italy; 14National Health and Lung Institute (NHLI), Imperial College London, London, UK; 15Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia; 16Woolcock Institute of Medical Research, Glebe, MSW, 2037, Australia; 17Institute of Allergology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; 18Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany; 19Respiratory Clinic, Department of Internal Medicine, University of Genoa, Genoa, Italy; 20IRCCS Ospedale Policlinico San Martino, Genoa, Italy; 21Optimum Patient Care Global, Cambridge, United Kingdom; Observational and Pragmatic Research Institute, Singapore; 22Division of Allergy and Immunology-Complexo Hospital de Clinicas-Department of Pediatrics-Federal University of Paraná, Curitiba, Brazil; 23Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy; 24Universidad Autónoma de Nuevo León, Faculty of Medicine and Hospital Universitario “Dr. José Eleuterio González”, Regional Center of Allergy and Clinical Immunology, Gonzalitos y Madero s/n Colonia Mitras Centro, Monterrey, Nuevo León, CP 64460, Mexico; 25Data & Analytics, NICE, Manchester, UK; 26School of Medicine, University of Nottingham, Nottingham, UK; 27American College of Chest Physicians, Glenview, IL, USA; 28Medicine Department and Allergy Unit, University of Malaga, Malaga, Spain; 29General Practitioners Research Institute, Groningen, the Netherlands; 30Groningen Research Institute Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 31Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 32Paul-Ehrlich-Institut, Langen, Germany; 33Allergy Center, CUF Descobertas Hospital, Lisbon, Portugal; 34Data Analytics Taskforce, EMA – European Medicine Agency, Amsterdam, the Netherlands; 35Department of Biomedical, Surgical and Dental Health Sciences, University of Milan, Milan, Italy; 36IRCCS Multimedica, Milan, Italy; 37Secretariat EFA- European Federation of Allergy and Airways Diseases, Brussels, Belgium; 38Allergy and Clinical Immunology Unit, Second Pediatric Clinic, National and Kapodistrian University of Athens, Athens, Greece; 39University of Manchester, Manchester, UK; 40Asia Pacific Association of AllergyAsthma Clinical Immunology, Nippon Medical School, Tokio, Japan; 41The Woolcock Institute of Medical Research and Macquarie University, Sydney, New South Wales, Australia; Faculty of Medicine and Health, The University of Sydney; Sydney Local Health District, Sydney, Australia; 42Hospital Juárez de México, CMICA y SLAAI, Ciudad de México, Mexico; 43Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK; 44Regional University Hospital, IBIMA-BIONAND, Malaga, Spain; 45Leadership Team, GAAPP – Global Allergy and Airways Patient Platform, Hendersonville, TN, USA; 46Department of Medicine, Boston University School of Medicine Boston, Boston, MA, USA*These authors contributed equally to this workCorrespondence: Stefano Del Giacco, Department of Medical Sciences and Public Health, Asse didattico “E1”, Cittadella Universitaria, Monserrato, Cagliari, 09042, Italy, Email delgiacco@unica.itAbstract: Integrating real-world evidence (RWE) into evidence-based medicine (EBM) enhances healthcare decision-making. RWE provides insights into the real-world effectiveness and safety of therapies and health technologies, filling gaps that clinical trials may leave. EBM, which concentrates on therapeutic issues, depends on rigorous evaluation of evidence, including data from randomized controlled trials (RCTs) and RWE. Combining evidence from RCTs and RWE when forming recommendations offers a comprehensive understanding of benefits and risks by considering their strengths, limitations, and standardized methods. The 2nd European Academy of Allergy & Clinical Immunology/Respiratory Effectiveness Group (EAACI/REG) Workshop, held in Rome, Italy, on October 4th, 2023, discussed integrating RWE and EBM. The goals were to develop recommendations for high-quality RWE and its inclusion in evidence syntheses, with a particular focus on airway diseases. During the discussion, key topics emerged. An “action plan” is needed to share these topics in various formats. RCTs are currently seen as providing the strongest evidence, so how to incorporate Non-Randomized Studies of Interventions (NRSI) requires careful consideration. An educational plan and collaboration with patients’ organizations are also very important. A collaborative approach involving patients, clinicians, and regulators is essential for achieving meaningful results and can be adapted as needed for cultural differences. A “glossary” of terms used in this context will be created to improve understanding. Setting benchmarks for data quality and reliability, such as quality thresholds, in disease-specific studies requires collaboration with research method experts. Managing and recording registries according to standardized protocols and quality standards from well-designed registries will ensure the data is valid and accurate.Keywords: real world evidence, evidence-based medicine, real world data, registries

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.369
GPT teacher head0.570
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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