Global prophylaxis trends in hemophilia: a macroeconomic analysis and its association with world development indicators
Bibliographic record
Abstract
INTRODUCTION: Prophylaxis is the recommended management strategy for all persons with hemophilia (PwH), yet its adoption is uneven worldwide. AREAS COVERED: This analysis examines global disparities in hemophilia care, focusing on global prophylactic coverage and its correlation with the World Bank's world development indicators. It outlines the disproportionate consumption of clotting factors and non-factor concentrates in high-income countries compared to lower-income counterparts and the challenges of expanding prophylaxis coverage in under-resourced settings. The analysis integrates socioeconomic data with global health indicators to understand these disparities and advocates for increased distribution of treatment resources across all income levels, emphasizing the need for policy changes to improve hemophilia care worldwide. Studies addressing the prophylaxis perspectives in hemophilia were selected using PubMed and Google Scholar platforms (unlimited time frame). Articles were supplemented with WFH's annual surveys and guidelines, including the WFH Global Survey 2022, WFH Guidelines for the Management of Hemophilia 2020 and World Bank data. EXPERT OPINION: Significant disparities in hemophilia care and factor usage exist between high-income and lower-income countries. Standardized, harmonized metrics for different types of factor consumption are critical to accurately assess and compare hemophilia care on an international basis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".