The global aHUS registry: methodology and initial patient characteristics
Bibliographic record
Abstract
BACKGROUND: Atypical hemolytic uremic syndrome (aHUS) is a rare, genetically-mediated systemic disease most often caused by chronic, uncontrolled complement activation that leads to systemic thrombotic microangiopathy (TMA) and renal and other end-organ damage. METHODS: The global aHUS Registry, initiated in April 2012, is an observational, noninterventional, multicenter registry designed to collect demographic characteristics, medical and disease history, treatment effectiveness and safety outcomes data for aHUS patients. The global aHUS Registry will operate for a minimum of 5 years of follow-up. Enrollment is open to all patients with a clinical diagnosis of aHUS, with no requirement for identified complement gene mutations, polymorphisms or autoantibodies or particular type of therapy/management. RESULTS: As of September 30, 2014, 516 patients from 16 countries were enrolled. At enrollment, 315 (61.0 %) were adults (≥18 years) and 201 (39.0 %) were <18 years of age. Mean (standard deviation [SD]) age at diagnosis was 22.7 (20.5) years. Nineteen percent of patients had a family history of aHUS, 60.3 % had received plasma exchange/plasma infusion, 59.5 % had a history of dialysis, and 19.6 % had received ≥1 kidney transplant. Overall, 305 patients (59.1 %) have received eculizumab. CONCLUSIONS: As enrollment and follow-up proceed, the global aHUS Registry is expected to yield valuable baseline, natural history, medical outcomes, treatment effectiveness and safety data from a diverse population of patients with aHUS. TRIAL REGISTRATION: US National Institutes of Health www.ClinicalTrials.gov Identifier NCT01522183 . Registered January 18, 2012.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".