The Influence of Activity Roles and Use of a Structured Framework on Developing Hematology Clinical Practice Guidelines
Notice bibliographique
Résumé
Abstract Introduction: The decision-making process during clinical recommendations development is central for producing high-quality hematology clinical practice guidelines, but little is known about how the activity roles of guidelines panelists (e.g., chair, methodologist, content expert, etc.) and the use of decision-making frameworks influence evidence-informed decisions when making clinical recommendations. Objective: To explore and describe the activity roles of panelists in hematology guidelines panels, the application of a structured decision-making framework, and the factors being considered by panels when making clinical recommendations. Methods: We conducted conventional and summative qualitative analyses on the decision-making process of 9 audio-recorded panels convened by the American Society of Hematology (ASH) to develop guidelines for the management of the following conditions: heparin-induced thrombocytopenia, thrombophilia, optimal management of anticoagulation therapy, venous thromboembolism (VTE) in pregnancy, in pediatric populations, in patients with cancer, in non-surgical patients, in surgical patients, and treatment in VTE. Each panel developed final recommendations through group consensus during face-to-face meetings using GRADE (Grading of Recommendations Assessment, Development, and Evaluation)'s Evidence-to-Decision (EtD) framework. For each recommendation, panels made explicit judgments for each criterion in the framework. We analyzed GRADE and non-GRADE criteria that were used to make each recommendation, as well as the activity roles of each panelist. Results: GRADE criteria occupied 95% of all deliberations. Over half (51.1%) of the panel deliberations concerned research evidence related to the clinical effects of a treatment or practice, followed by discussion on resource use and costs (16.7%), feasibility and acceptability (13.5%), risks of benefits and harms (8.8%), equity (4.0%), and values and preferences (1.0%). Non-GRADE criteria represented the remaining 5% of the discussions (transparent communication on the decision-making process when making recommendations, legal implications, political context, and clinical experience). Chairs and co-chairs actively led and facilitated all discussion topics; they contributed to over half of the deliberations (55.2%). The remaining deliberations were from panelists (38.1%), systematic review team members (5.0%), and patient representatives (1.0%). Conclusions: The application of the EtD framework provided a highly structured decision-making process when making clinical recommendations for hematologic conditions. Chairs and co-chairs tend to actively lead the panel discussions, which contributed to framework adherence. The optimal role of chairs and co-chairs versus other panelists need to be further investigated. Future studies should examine how the decision-making process of treatment and interventions for hematologic conditions differ between guidelines panels that use and do not use a structured framework to develop clinical practice recommendations. Disclosures Cuker: Genzyme: Consultancy; Synergy: Consultancy; Spark Therapeutics: Research Funding; Kedrion: Membership on an entity's Board of Directors or advisory committees.
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 | 0,214 | 0,411 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».