NHF‐McMaster Guideline on Care Models for Haemophilia Management
Bibliographic record
Abstract
This guideline was developed to identify evidence-based best practices in haemophilia care delivery, and discuss the range of care providers and services that are most important to optimize outcomes for persons with haemophilia (PWH) across the United States. The guideline was developed following specific methods described in detail in this supplement and based on the GRADE (Grading of Recommendations, Assessment, Development and Evaluation approach). Direct evidence from published literature and the haemophilia community, as well as indirect evidence from other chronic diseases, were reviewed, synthesized and applied to create evidence-based recommendations. The Guideline panel suggests that the integrated care model be used over non-integrated care models for PWH (conditional recommendation, moderate certainty in the evidence). For PWH with inhibitors and those at high risk for inhibitor development, the same recommendation was graded as strong, with moderate certainty in the evidence. The panel suggests that a haematologist, a specialized haemophilia nurse, a physical therapist, a social worker and round-the-clock access to a specialized coagulation laboratory be part of the integrated care team, over an integrated care team that does not include all of these components (conditional recommendation, very low certainty in the evidence). Based on available evidence, the integrated model of care in its current structure, is suggested for optimal care of PWH. There is a need for further appropriately designed studies that address unanswered questions about specific outcomes and the optimal structure of the integrated care delivery model in haemophilia.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".