Assessment of Treatment Practice Patterns for Severe Hemophilia A: A Global Nurse Perspective
Bibliographic record
Abstract
This paper reports findings from a global survey of practice patterns for severe hemophilia A. Nurses from 105 hemophilia treatment centers in the US, the UK, Canada and Sweden responded to a questionnaire and provided data for more than 10,100 children and adults. Forty-eight percent of the US patients and 38 and 37% of the British and Swedish patients, respectively, were reported to have severe hemophilia A. The survey found that 28% of US patients and 38% of UK patients with severe hemophilia A were on primary prophylaxis in 2005. These rates were significantly higher than those reported in a 2003 survey. Sweden continues to lead the world in prophylaxis utilization, with virtually 100% of patients aged 3-18 on primary prophylactic regimens. Bleeding history and target joint development were major reasons for initiating prophylaxis; poor adherence, inadequate family commitment and venous access problems were cited as the top causes for discontinuing treatment. Nurses in all 4 reporting countries agreed that prophylaxis is the optimal therapy for patients with severe hemophilia A because it prevents joint and muscle damage and improves quality of life. They cited patient/family education as the most appropriate strategy for overcoming the barriers to prophylaxis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".