The World Federation of Hemophilia World Bleeding Disorders Registry: insights from the first 10,000 patients
Bibliographic record
Abstract
Background: The prevalence of hemophilia varies globally, with close to 100% of patients diagnosed in high-income countries and as low as 12% diagnosed in lower-income countries. These inequalities in the care of people with hemophilia exist across various care indicators. Objectives: This analysis aims to describe the clinical care outcomes of patients in the World Bleeding Disorders Registry (WBDR). Methods: In 2018, the World Federation of Hemophilia developed a global registry, the WBDR, to permit hemophilia treatment centers to collect clinical data, monitor patient care longitudinally, and identify gaps in management and treatment. Results: = 5084) of patients had severe hemophilia; 99% were male, 85% had hemophilia A, and 67% were from low-middle-income countries. Globally, the age of diagnosis for people with severe hemophilia has improved considerably over the last 50 years, from 82 months (∼7 years) for those born before 1980 to 11 months for those born after 2010, and most prominently, among people with severe hemophilia in low- and low-middle-income countries, the age of diagnosis improved from 418 months (∼35 years) for those born before 1970 to 12 months for those born after 2010. Overall, the age of diagnosis of people with hemophilia in low- and low-middle-income countries is delayed by 3 decades compared to patients in upper-middle-income countries and by 4 decades compared to patients in high-income countries. Conclusion: Data reveal large treatment and care disparities between socioeconomic groups, showing improvements when prophylaxis is initiated to prevent bleeding. Overall, care provided in low-income countries lags behind high-income countries by up to 40 years. Limitations in the interpretation of data include risk of survival and selection bias.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".