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Record W4413353216 · doi:10.1016/j.eclinm.2025.103422

Development of a core patient-centred outcome set for adults living with obesity: a modified delphi-based international consensus

2025· article· en· W4413353216 on OpenAlexafffund
Teferi Mekonnen, Leanne J. Staniford, S. Connell, Zofia Das‐Gupta, Umanga DeSilva, Yasmine Saoud, Isabel Miller, Mohapradeep Mohan, Simon W. Nienhuijs, Megha Poddar, Vaishali Deshmukh, M Conradie-Smit, Soo Huat Teoh, Sanjeev Sockalingam, Emilia Huvinen, Paolo Sbraccia, Ronald S. L. Liem, Michael Vallis, Ken Clare, Tracy Zvenyach, Karen Coulman, Marianela Aguirre Ackermann, Verónica Vázquez‐Velázquez, Yudith Preiss Contreras, Josép Vidal, Urudinachi Nnenne Agbo, María Fernanda Tejeda-Muñoz, Ian Patton, T. Alafia Samuels, Morgan Emile Gabriel Salmon Leguede, Georgia Rigas, Ximena Ramos Salas, Michael Crotty, Bruno Halpern, Daniel H. Bessesen, Laura Pizzi, Omniyat Mohammed Al Hajeri, Arya M. Sharma

Bibliographic record

VenueEClinicalMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of AlbertaUniversity of TorontoDalhousie UniversityCanadian Obesity NetworkCentre for Addiction and Mental HealthMcMaster University
FundersNIHR School for Primary Care ResearchNovo NordiskNational Institute for Health and Care ResearchAmryt PharmaGedeon RichterBoehringer IngelheimCanadian Cardiovascular SocietyPfizerNational Institute of Academic AnaesthesiaObesity CanadaEli Lilly and Company
KeywordsMedicineDelphi methodCore (optical fiber)DelphiOutcome (game theory)Set (abstract data type)ObesityFamily medicineGerontologyArtificial intelligenceInternal medicine

Abstract

fetched live from OpenAlex

Background: Obesity is a chronic disease linked to over 200 health conditions, reduced quality of life, and increased mortality. Despite the availability of multimodal treatments, there is a lack of standardised, patient-centred outcome measures to effectively assess and improve clinical care. This project aimed to define a core set of standardised outcome measures for adults with obesity, incorporating both patient-reported and clinician-reported outcomes. Additional objectives included determining the optimal measurement frequency and identifying case-mix variables essential for risk adjustment. Methods: The International Consortium for Health Outcomes Measurement (ICHOM) established the Obesity Working Group (OWG), composed of 29 international experts and individuals living with obesity. Members were selected based on their expertise in obesity care and research and represented 21 countries. The development process, conducted from May 2023 to July 2024, began with a kick-off meeting to define scope, followed by eight virtual meetings. A comprehensive literature review informed the identification of relevant clinical outcomes, patient-reported outcomes, treatment-related complications, and case-mix variables. A three-round Delphi process was used to reach consensus on key outcomes and follow-up intervals. Outcomes were included in the final set if at least 80% of OWG members rated them between 7 and 9 on a 9-point scale. Validation was conducted through surveys with 95 patients and 106 healthcare professionals from fields such as dietetics, obesity medicine, and general practice. Findings: The OWG developed a standardised set of 20 outcome measures, comprising 17 core measures and 3 population-specific measures. These span domains including physical health, psychosocial well-being, health behaviours, body functioning, and adverse events. Specific measures were also developed for bariatric surgery and female reproductive health (both pregnant and non-pregnant individuals). Key case-mix factors and appropriate follow-up periods were defined to support global consistency in reporting. Interpretation: Adoption of this standardised outcome set enables clinicians, patients, and stakeholders to identify care gaps, monitor outcomes, and evaluate the quality of obesity care against global benchmarks. Implementation can support the creation of a robust international data resource to guide research, inform clinical practice, and shape policy and guidelines for adult obesity treatment. Limitations include limited geographical diversity in validation surveys, the exclusion of internalised weight bias due to a lack of validated measurement tools, and limited applicability to individuals with obesity and multiple comorbidities. Funding: Novo Nordisk, Eli Lilly, and Boehringer Ingelheim International GmbH.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.351
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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