Improving the Transparency and Replicability of Consensus Methods: Respiratory Medicine as a Case Example
Bibliographic record
Abstract
Pragmatic and Observational Research strongly encourages all authors reporting the results of studies using consensus methods to follow the ACcurate COnsensus Reporting Document (ACCORD) guideline 1,2 to ensure consistent, transparent reporting with sufficient detail to allow study replication of consensus methods and informed interpretation of the results.Consensus studies play a critical role in biomedicine, supporting pragmatic decision-making in areas in which the existing evidence is equivocal, limited, absent, or still developing.1,[3][4][5][6] Consensus approaches generally use iterative processes to synthesize expert opinions so that outputs are based on the collective knowledge and expertise of participants.7,8 Formal methodologies exist to guide and optimize the process of achieving consensus, 7 such as the Delphi method, 9,10 nominal group technique (NGT), 11 RAND/UCLA Appropriateness Method, 12 and structured consensus meetings.13 These established consensus methods differ in terms of anonymity, group size, and the nature of participant interactions (eg, face-to-face vs virtual meetings, or no meetings), allowing the appropriate method to be selected in the context of specific research questions and settings.Regardless of any differences, all formal methodologies generally aim to engage relevant stakeholders, encourage equitable contributions from participants, and minimize potential sources of bias.7 Consensus processes, especially the Delphi method, are well established in biomedicine and widely published in the scientific literature.A targeted search of the MEDLINE bibliographic database (conducted July 16, 2024) for publications on consensus conferences, NGT, Delphi, or RAND/UCLA methods identified 27,235 publications since 1946, of which 6117 (22%) were published in the period January 1, 2020 to July 16, 2024 (see Figure 1).The utility and adaptability of consensus methods were apparent when the identified publications were considered by type and across therapy areas.For example, 2048 of the consensus studies published since 2020 relate to respiratory medicine, which is a therapy area that spans acute and chronic conditions and communicable and noncommunicable diseases, affects individuals across the age spectrum, and contributes significantly to global mortality.14 Within respiratory medicine, it was evident from the literature that consensus methodologies have been used to: facilitate disease diagnosis and management; 15,16 assess treatment choice (including delivery method, dosing, and duration); [17][18][19] define outcome measurements; 20 assess research priorities; 21 confirm diagnostic quality indicators and assessment guidelines; 22 guide the development of electronic patient records; 23 define registry data collection criteria; 6 validate prognostic models; 24 and establish disease definitions.[25][26][27][28][29] Specific examples include the use of consensus studies to generate clinical recommendations on the optimal assessment and management of chronic obstructive pulmonary disease 30,31 and the selection of candidates for lung transplantation, 32 to inform treatment and research priorities in pediatric acute respiratory distress syndrome, 33 to guide selection and use of inhaler devices, 17,19 to assist primary care diagnosis of respiratory diseases, 34 and to Pragmatic and Observational
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.136 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".