All‐cause mortality and causes of death in persons with haemophilia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction Improvements in haemophilia treatment over the last decades resulted in increased life expectancy in persons with haemophilia (PWH). Aim We conducted a systematic review and meta‐analysis to examine all‐cause mortality and causes of death among PWH. Methods We systematically searched EMBASE, MEDLINE, Web of Science, CINAHL and Cochrane central register of controlled trials from inception through March 15, 2021. Studies that reported a mortality estimate of PWH compared with the general population and/or reported causes of death were included. Random‐effects meta‐analysis with inverse variance method was used to obtain pooled estimates. We stratified the analysis by the year of cohort entry (before 2000 vs after 2000). Result Of the 4769 studies identified, 52 met the eligibility criteria. The pooled all‐cause standardized mortality ratio (SMR) from 9 studies in PWH was 1.93 (95% CI 1.38–2.70; I2 = 97%). The pooled SMRs before and after the year 2000 were 2.40 (95% CI 1.92–3.00; I2 = 87%) and 1.20 (95% CI 1.03–1.40; I2 = 62%), respectively. Before the year 2000, 31.2% deaths occurred due to HIV followed by haemorrhage (26.0%), cardiovascular disease (18.2%), liver disease (9.0%), and cancer (8.9%). Fewer (13.9%) deaths were attributable to HIV after the year 2000 with the proportion of deaths due to haemorrhage remaining unchanged. Conclusion With treatment advances, mortality in PWH has declined over the last few decades approaching that of the general population. However, haemorrhage remains a leading cause of death requiring further attention.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".