Renal and multisystem effectiveness of 3.9 years of migalastat in a global real‐world cohort: Results from the <scp>followME</scp> Fabry <scp>Pathfinders</scp> registry
Bibliographic record
Abstract
Abstract Fabry disease is a progressive, X‐linked lysosomal disorder caused by reduced or absent α‐galactosidase A activity due to GLA variants. The effects of migalastat were examined in a cohort of 125 Fabry patients with migalastat‐amenable GLA variants in the followME Pathfinders registry (EUPAS20599), an ongoing, prospective, patient‐focused registry evaluating outcomes for current Fabry disease treatments. We report annualised estimated glomerular filtration rate (eGFR) and Fabry‐associated clinical events (FACEs) in a cohort of patients who had received ≥3 years of migalastat treatment in a real‐world setting. As of August 2022, 125 patients (60% male) had a mean migalastat exposure of 3.9 years. At enrolment, median age was 58 years (males, 57; females, 60) with a mean eGFR of 83.7 mL/min/1.73 m2 (n = 122; males, 83.7; females, 83.8) and a median left ventricular mass index of 115.1 g/m2 (n = 61; males, 131.2; females, 98.0). Mean (95% confidence interval) eGFR annualised rate of change in the overall cohort (n = 116) was −0.9 (−10.8, 9.9) mL/min/1.73 m2/year with a similar rate of change observed across patients with varying levels of kidney function at enrolment. Despite population age and baseline morbidity, 80% of patients did not experience a FACE during the mean 3.9 years of migalastat exposure. The incidence of renal, cardiac, and cerebrovascular events was 2.0, 83.2, and 4.1 events per 1000 patient‐years, respectively. These data support a role of migalastat in preserving renal function and multisystem effectiveness during ≥3 years of migalastat treatment in this real‐world Fabry population.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".