Methodology for the development of the <scp>NHF</scp>‐McMaster Guideline on Care Models for Haemophilia Management
Bibliographic record
Abstract
BACKGROUND: Rigorous and transparent methods are necessary to develop clinically relevant and evidence-based practice guidelines. We describe the development of the National Hemophilia Foundation-McMaster Guideline on Care Models for Haemophilia Management, which addresses best practices in haemophilia care delivery. METHODS: We assembled a Panel of persons with haemophilia (PWH), parents of PWH, clinical experts and guideline methodologists. Conflicts of interest were disclosed and managed throughout. Panel members and key stakeholders were surveyed to develop the guideline questions and identify patient-important outcomes. Systematic reviews of the literature were conducted for all factors important in decision-making: benefits and harms; patient values and preferences; resource implications; acceptability; equity; and feasibility. We used the GRADE approach to create evidence profiles to evaluate the evidence and present key results. Evidence to Decision frameworks were created to guide the Panel in making evidence-based recommendations. When evidence was very low quality or not available, evidence from other chronic disease populations was presented to the Panel to inform the recommendations. Additionally, we systematically pooled observations from experts, and conducted qualitative interviews exploring key stakeholder experiences and perspectives. The Panel made recommendations for each guideline question and elaborated on research priorities, implementation considerations, and monitoring. Final recommendations were circulated for public and peer review. CONCLUSIONS: Despite the paucity of high-quality evidence typical of a rare condition such as haemophilia, we successfully applied a rigorous and transparent methodology based on GRADE to develop an evidence-based clinical practice guideline.
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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 | 0.157 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".