A study of variations in the reported haemophilia B prevalence around the world
Bibliographic record
Abstract
Summary. The objectives of this article were to study the reported prevalence of haemophilia B (HB) on a country‐by‐country basis and to analyse whether the prevalence of HB varied by national economy. The prevalence of HB is the proportion of diagnosed, reported cases of HB in a population at a specific point of time. We collected data on the HB prevalence for 105 countries from the World Federation of Hemophilia annual global surveys. Our results showed that the HB prevalence varied considerably among countries, even among the wealthiest of countries. The HB prevalence (per 100 000 males) for the highest income countries was 2.69 ± 1.61 (mean ± SD), whereas the prevalence for the rest of the world was 1.20 ± 1.33 (mean ± SD). Ireland had the highest reported HB prevalence of 8.07 per 100 000 males. There was a strong trend of increasing HB prevalence (per 100 000 males) over time. Prevalence data reported from the WFH compared well with prevalence data from the literature. The WFH annual global surveys have some limitations, but they are the best available source of worldwide haemophilia data. Prevalence data are extremely valuable information for the planning efforts of national healthcare agencies in setting priorities and allocating resources for the treatment of HB.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".