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Record W7115718224 · doi:10.48448/s878-pq12

Challenges in achieving uptake and journal endorsement of the ACcurate COnsensus Reporting Document (ACCORD) guideline

2025· other· W7115718224 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelinePublishingSteering committeeDiscretionMEDLINEBibliometrics

Abstract

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Christopher C. Winchester,1 Mark J. Rolfe,1 William T. Gattrell,2 Patricia Logullo,3 Keith Goldman,4 Amy Price,5 Paul Blazey,6 Esther J. van Zuuren,7 Niall Harrison8 Objective The ACcurate COnsensus Reporting Document (ACCORD) guideline supports the reporting of biomedical studies involving any consensus method.1 We evaluated its uptake by bibliometric analysis and qualitative and quantitative assessment of implementation activities 1 year post publication. Design An implementation plan2 was established by the ACCORD Steering Committee before guideline publication, including developing an explanation and elaboration (E&E) document, writing journal editorials, presenting at congresses, and encouraging journals and publishers to include ACCORD in author instructions. Guideline views and downloads (via PLOS Medicine,1 the publishing journal) and citations (Dimensions, plus additional references from the Steering Committee’s reference libraries) were obtained for the period from January 23, 2024, to January 22, 2025. Likely journal and publisher beneficiaries of ACCORD were identified by a literature search to determine the 10 journals that published the most consensus studies (Web of Science for 2020-2023) and other publications at the discretion of the Steering Committee. Relevant journal and publisher stakeholders were contacted directly or indirectly (eg, via journal websites). Activities were evaluated against the implementation plan2; journal and publisher responses were described. Results In the 1 year following publication, the ACCORD guideline was viewed 13,752 times, downloaded (full-text PDF) 3790 times, and cited 105 times (5 citations [4.8%] were in articles by ACCORD authors, including the ACCORD E&E3). Sources of guideline citations included consensus studies (67.6% [71 of 105]), reviews (11.4% [12 of 105]), study protocols (8.6% [9 of 105]), other reporting standards and guidelines (6.7% [7 of 105]), and editorials (3.8% [4 of 105]). Of the journals contacted by ACCORD Steering Committee members (between August 24, 2023 [preprint publication], and January 22, 2025), BMJ Open, British Journal of Dermatology, and Journal of Clinical Epidemiology mandated the use of ACCORD for consensus studies, while the British Journal of Sports Medicine, Internal Medicine Journal, and Pragmatic and Observational Research encouraged its use. PLOS has confirmed its intention to include the guideline in their author instructions. Although a further 4 journals have expressed interest in ACCORD and 1 publisher has offered to raise awareness via a webinar, they are yet to commit to its inclusion in author instructions; 2 journals and publishers have not responded to inquiries. Conclusions One year after publication, the ACCORD guideline has been cited and used to inform the design and reporting of consensus-based research. However, journal and publisher adoption has been limited, which may affect long-term uptake. Given the resources invested in developing reporting guidelines and their potential to improve reporting, the role of biomedical journals and publishers in their adoption warrants wider discussion. References 1. Gattrell WT, Logullo P, van Zuuren EJ, et al. ACCORD (ACcurate COnsensus Reporting Document): a reporting guideline for consensus methods in biomedicine developed via a modified Delphi. PLOS Med. 2024;21:e1004326. doi:10.1371/journal.pmed.1004326 2. Gattrell WT, Hungin AP, Price A, et al. ACCORD guideline for reporting consensus-based methods in biomedical research and clinical practice: a study protocol. Res Integr Peer Rev. 2022;7:3. doi:10.1186/s41073-022-00122-0 3. Logullo P, van Zuuren EJ, Winchester CC, et al. ACcurate COnsensus Reporting Document (ACCORD) explanation and elaboration: guidance and examples to support reporting consensus methods. PLOS Med. 2024;21:e1004390. doi:10.1371/journal.pmed.1004390 1Oxford PharmaGenesis, Oxford, UK, chris.winchester@pharmagenesis.com; 2Independent Medical Communications Professional, Oxfordshire, UK; 3Bodleian Libraries, University of Oxford, Oxford, UK; 4Medical Affairs + Health Impact, AbbVie, North Chicago, IL, US; 5Dartmouth Institute for Health Policy & Clinical Practice (TDI), Geisel School of Medicine, Dartmouth College, Hanover, NH, US; 6School of Kinesiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; 7Leiden University Medical Centre, Leiden, the Netherlands; 8OPEN Health Communications, London, UK. Conflict of Interest Disclosures Christopher C. Winchester is an employee, director, and shareholder of Oxford PharmaGenesis; a director of Oxford Health Policy Forum CIC; a trustee of the Friends of the National Library of Medicine; and an associate fellow of Green Templeton College, University of Oxford. Mark J. Rolfe is an employee of Oxford PharmaGenesis. William T. Gattrell was an independent medical communications professional at the time of this study and is currently an employee of Bristol Myers Squibb. Keith Goldman is an employee of AbbVie. Niall Harrison is an employee of OPEN Health Communications. Funding/Support Medical writing, editorial, and project management support for this abstract were provided by Oxford PharmaGenesis. Acknowledgments Medical writing support was provided by Alison Chisholm, with editorial support from Jenny Thorp, and administrative support was provided by Mehraj Ahmed, Ryan Gamble, and Jessica Miller. Additional Information No authors were reimbursed for participating in the initiative.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.788
metaresearch head score (Gemma)0.907
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.212
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7880.907
Meta-epidemiology (narrow)0.0010.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0140.013
Science and technology studies0.0090.012
Scholarly communication0.0410.025
Open science0.0160.024
Research integrity0.0230.025
Insufficient payload (model declined to judge)0.0070.006

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 head0.089
GPT teacher head0.364
Teacher spread0.276 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
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
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