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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".