Improving the Transparency and Replicability of Consensus Methods: Respiratory Medicine as a Case Example
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,136 | 0,505 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,023 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».