The Gaucher Registry
Bibliographic record
Abstract
BACKGROUND: The Gaucher Registry, the largest database of patients with Gaucher disease (GD) worldwide, was initiated to better delineate the progressive nature of the disorder and determine optimal therapy. This report describes the demographic and clinical characteristics of 1698 patients with GD before they received enzyme replacement therapy. METHODS: Physicians worldwide who treat patients with GD were invited to submit prospective and retrospective data for an ongoing registry, using standardized data collection forms, for central processing and review. RESULTS: Most patients were from the United States (45%) and Israel (17%), but patients are from 38 countries. Most (94%) had type 1 GD, fewer than 1% had type 2, and 5% had type 3. Mutant allele frequency data, available for 45% of patients, showed the most common alleles to be N370S (53%), L444P (18%), 84GG (7%), and IVS2+1 (2%). Twenty-five percent of L444P homozygotes (13 of 52 patients) had type 1 GD phenotype. Mean age at diagnosis in patients with the N370S/N370S genotype was 27.2 years (SD, 19.7 years); in L444P/L444P patients, 2. 3 years (SD, 3.2 years). Histories of bone pain and radiological bone disease were reported by 63% and 94% of patients, respectively; both were more likely in asplenic patients than in patients with spleens. Mean spleen and liver volumes were 19.8 and 2.0 multiples of normal, respectively. Anemia and thrombocytopenia were present in 64% and 56%, respectively. Thrombocytopenia was present in 13% of asplenic patients. CONCLUSIONS: The Gaucher Registry permits a comprehensive understanding of the clinical spectrum of GD because of the uniquely large sample size. The Registry will be useful in evaluating the effects of specific therapies in GD and the possible influences of environment, ethnicity, and genotype on the natural history of the disorder.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.038 |
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".