The Influence of Activity Roles and Use of a Structured Framework on Developing Hematology Clinical Practice Guidelines
Bibliographic record
Abstract
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.
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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 | 0.214 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".