A Global Cross‐Sectional Database Study of Low Dose FVIII SHL Prophylaxis in Haemophilia A
Bibliographic record
Abstract
ABSTRACT Introduction Haemophilia treatment is costly and only 25% of patients receive adequate care. Although not optimal, Factor VIII (FVIII) low‐dose prophylaxis (LDP) may reduce annual joint bleeding rates. Understanding FVIII usage, collected through the Web‐Accessible Population Pharmacokinetic Service–Hemophilia (WAPPS‐Hemo) platform, and its association with Gross National Income per capita (GNI) and Universal Health Coverage index (UHCI) may provide insights in global disparities. Aims To provide insights in FVIII use to advocate for LDP in low‐income countries by providing: (i) statistical summary of FVIII usage, LDP prevalence, GNI and UHCI in WAPPS‐Hemo in 2017–2023; (ii) estimation of the relationship between LDP probability ( P LDP ) and GNI/UHCI for children (≤12 years) and adults; (iii) exploratory comparison of pharmacokinetics (PKs) across LDP/non‐LDP. Methods Descriptive statistics/graphical summaries for (i) and (iii), mixed‐effects logistic regression for (ii). Results Data from 6223 severe haemophilia patients (ages 0.1–92 years) showed that 18% and 32% of countries used ≤1% LDP infusions in children and adults. LDP prevalence rose annually, peaking at 7% for children and 14% for adults. GNI was found lower in LDP‐prevalent countries in children. In both children and adults, P LDP demonstrated an inverse association with GNI and UHCI. PK outcomes were similar across LDP status, except potentially for plasma‐derived products in children, however limited by sample size. Conclusion Underrepresentation of low‐resource countries in WAPPS‐Hemo underscores the financial challenges in haemophilia treatment. The association between GNI/UHCI and P LDP suggests cost‐driven adoption of LDP in low‐resource settings, especially in children. PK outcomes average similarities may facilitate LDP‐usage in WAPPS‐Hemo. Trial Registration NCT02061072, NCT03533504 (ClinicalTrials.gov)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".