A systematic literature review to evaluate the cardiac and cerebrovascular outcomes of patients with Fabry disease treated with agalsidase Beta
Bibliographic record
Abstract
Background Agalsidase beta is used to treat Fabry disease (FD); however, data on cardiac and cerebrovascular outcomes with agalsidase beta treatment come from studies with limited numbers of patients. Methods A systematic literature review of studies reporting on the efficacy and effectiveness of agalsidase beta in FD was conducted. Studies were identified in searches of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials from January 2000–June 2022. Outcomes of interest included cardiac structure and mass, cardiac events, and cerebrovascular events. Results Fifty-two citations (41 studies) were included. Reductions in interventricular septal thickness (IVST) and/or left ventricular posterior wall thickness (LVPWT) were demonstrated in six studies (follow-up 1–6 years, n = 4 using echocardiography, n = 2 cardiac MRI). IVST ranged from 12.1–14.9 mm at baseline and 10.8–14.1 mm at follow-up (all p < 0.05). LVPWT ranged from 11.7–16.0 mm at baseline and 10.7–13.0 mm at follow-up (all p < 0.05). Significant reductions in cardiac mass were demonstrated after 1 year of treatment in a single-arm study using cardiac MRI [left ventricular mass (LVM) 193–178 g; LVM index 102–94 g/m2; both p < 0.05]. Rates of composite cardiac events (3.8%–24.0%; four studies, follow-up 2–10 years) and cerebrovascular events (0.0%–18.9%; 12 studies, follow-up 1–10 years) were numerically lower than rates for placebo (follow-up 3 years). Conclusion Literature over the last 20 years indicates that agalsidase beta treatment may lead to stabilization or regression of cardiac structural thickness and mass, and reduction in cardiac and cerebrovascular events relative to placebo.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".