Twenty years of the Fabry Outcome Survey (FOS): insights, achievements, and lessons learned from a global patient registry
Bibliographic record
Abstract
BACKGROUND: Patient registries provide long-term, real-world evidence that aids the understanding of the natural history and progression of disease, and the effects of treatment on large patient populations with rare diseases. The year 2021 marks the 20th anniversary of the Fabry Outcome Survey (FOS), an international, multicenter, observational registry (NCT03289065). The primary aims of FOS are to broaden the understanding of Fabry disease (FD), an X-linked lysosomal storage disorder, and to improve the clinical management of affected patients. Here, we review the history of FOS and the analyses and publications disseminated from the registry, and we discuss the contributions FOS studies have made in understanding FD. RESULTS: FOS was initiated in April 2001 and, as of January 2021, 4484 patients with a confirmed diagnosis and patient informed consent have been enrolled from 144 centers across 26 countries. Data from FOS have been published in nearly 60 manuscripts on a wide variety of topics relevant to FD. Analyses of FOS data have investigated the long-term effectiveness and safety of enzyme replacement therapy (ERT) with agalsidase alfa and its effects on morbidity and mortality, as well as the benefits of prompt and early treatment with agalsidase alfa on the progression of cardiomyopathy and the decline in renal function associated with FD. Based on analyses of FOS data, ERT with agalsidase alfa has also been shown to improve additional signs and symptoms of FD experienced by patients. FOS data analyses have provided a better understanding of the natural history of FD and the specific populations of women, children, and the elderly, and have provided practical tools for the study of FD. FOS has also provided methodology and criteria for assessing disease severity which contributed to the continuous development of medical practice in FD and has largely improved our understanding of the challenges and needs of long-term data collection in rare diseases, aiding in future rare disease real-world evidence studies. CONCLUSION: FOS over the last 20 years has substantially increased the scientific knowledge around improved patient management of FD and continues to expand our understanding of this rare disease.
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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 | 0.064 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".