Integrated Hemophilia Patient Care via a National Network of Care Centers in the United States: A Model for Rare Coagulation Disorders
Bibliographic record
Abstract
Rare, chronic diseases such as hemophilia and other congenital coagulation disorders require coordinated delivery of services for optimal outcomes. Hemophilia Treatment Centers (HTCs) are specialized, multidisciplinary health-care centers providing team-based care to meet the physical, psychosocial, and emotional needs of people with hemophilia (PWH) and may serve as a model for other rare coagulation disorders. Health-care purchasers, as well as the general medical community, may not appreciate the breadth and quality of services provided by HTCs. They exemplify the acculturalization and actualization of integrated care by providing comprehensive diagnostic and treatment services that reduce morbidity, mortality, avoidable emergency room visits, hospitalizations, and overall costs, while promoting a longer lifespan and improved patient functioning and outcomes. This is accomplished by a team-based approach relying upon a shared decision-making model to effectively prevent complications and manage symptoms in PWH, who are dependent on high-cost treatments. This article provides a concise yet comprehensive description of the core components of an HTC and the regional and national networks in the United States, which together achieve their incomparable value for all stakeholders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".