Establishing the Prevalence and Prevalence at Birth of Hemophilia in Males
Bibliographic record
Abstract
Background: The large observed variability in hemophilia prevalence prevents robust estimation of burden of disease. Objective: To estimate the prevalence and prevalence at birth of hemophilia and the associated life expectancy disadvantage. Design: Random-effects meta-analysis of registry data. Setting: Australia, Canada, France, Italy, New Zealand, and the United Kingdom. Participants: Male patients with hemophilia A or B. Measurements: Prevalence of hemophilia as a proportion of cases to the male population, prevalence of hemophilia at birth as a proportion of cases to live male births by year of birth, life expectancy disadvantage as a 1 - ratio of prevalence to prevalence at birth, and expected number of patients worldwide based on prevalence in high-income countries and prevalence at birth. Results: Prevalence (per 100 000 males) is 17.1 cases for all severities of hemophilia A, 6.0 cases for severe hemophilia A, 3.8 cases for all severities of hemophilia B, and 1.1 cases for severe hemophilia B. Prevalence at birth (per 100 000 males) is 24.6 cases for all severities of hemophilia A, 9.5 cases for severe hemophilia A, 5.0 cases for all severities of hemophilia B, and 1.5 cases for severe hemophilia B. The life expectancy disadvantage for high-income countries is 30% for hemophilia A, 37% for severe hemophilia A, 24% for hemophilia B, and 27% for severe hemophilia B. The expected number of patients with hemophilia worldwide is 1 125 000, of whom 418 000 should have severe hemophilia. Limitation: Details were insufficient to adjust for comorbid conditions and ethnicity. Conclusion: The prevalence of hemophilia is higher than previously estimated. Patients with hemophilia still have a life expectancy disadvantage. Establishing prevalence at birth is a milestone toward assessing years of life lost, years of life with disability, and burden of disease. Primary Funding Source: None.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".