Use of a rare disease registry for establishing phenotypic classification of previously unassigned <i>GLA</i> variants: a consensus classification system by a multispecialty Fabry disease genotype–phenotype workgroup
Bibliographic record
Abstract
Background Fabry disease (α-galactosidase deficiency) is an X-linked genetic disease caused by a variety of pathogenic GLA variants. The phenotypic heterogeneity is considerable, with two major forms, classic and later-onset disease, but adjudication of clinical phenotype is currently lacking for many variants. We aimed to determine consensus phenotypic classification for previously unclassified GLA variants from the GLA-specific fabry-database.org database. Methods A Fabry disease genotype–phenotype workgroup developed a five-stage iterative system based on expert clinical assessment, published literature and clinical evidence of pathogenicity using a 2-point scoring system based on clinical hallmarks of classic disease. Kaplan–Meier (KM) analysis of severe clinical event-free survival was used as final validation. Results were compared with those from web-based disease databases and in silico pathogenicity prediction programmes. Results Final consensus on classifications of ‘pathogenic’ was achieved for 32 of 33 GLA variants (26 ‘classic’ phenotype, 171 males; 6 ‘later-onset’ phenotype, 57 males). One variant remained of uncertain significance. KM curves were similar for the known fabry-database.org database phenotypes and when workgroup consensus classifications were added, and the curves retained the same separation between ‘classic’ and ‘later-onset’ phenotypes. Conclusion The iterative system implemented by a Fabry disease genotype–phenotype workgroup achieved phenotypic classifications for variants that were previously unclassified. Clinical pathogenicity associated with a particular GLA variant defined in affected males appears to have predictive value and also generally correlates with risk for affected females. The newly established classifications can be of benefit to the clinical care of Fabry patients harbouring these variants.
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".