The World Federation of Hemophilia Annual Global Survey 1999‐2018
Bibliographic record
Abstract
INTRODUCTION: The World Federation of Hemophilia (WFH) strives to achieve care for all patients with inherited bleeding disorders through research, advocacy, capacity building and education. The WFH developed and implemented the Annual Global Survey (AGS), through which comprehensive demographic and treatment data on bleeding disorders are collected each year from its constituent non-governmental national organizations. AIM: To describe the development, methodology and achievements of the WFH AGS over the past 20 years. METHODS: The AGS is a yearly cross-sectional survey. Data are collected using a standardized form (available online and on paper), quality checked and reviewed, and published in English, French and Spanish. Over time, the AGS has been modified in response to changes in treatment landscape or emerging new issues. RESULTS: Over the past 20 years, the AGS has reported an increase in the number of countries participating in the survey, a tripling in the number of people identified with rare bleeding disorders and an increase in the amount of factor used to treat people with haemophilia. Yet, a large treatment inequity gap still exists across the globe. In response to this gap, the WFH has analysed the AGS reports which has stimulated further development in quality of care indicators, estimates of the global prevalence of haemophilia, patient-level data collection efforts like the World Bleeding Disorders Registry and the Gene Therapy Registry. CONCLUSION: The AGS has provided evidence to support research, programme planning and development activities of the WFH.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".