Multidisciplinary consensus on the diagnosis and management of patients with atypical Hemolytic Uremic Syndrome (complement-mediated TMA): Recommendations from Italian scientific societies, patient associations and regulators
Bibliographic record
Abstract
Atypical Hemolytic Uremic Syndrome (aHUS) is a severe, systemic, rare disease (RD) that can occur in people of all ages, and is associated with high rates of morbidity and mortality. Because the management of patients with aHUS can be difficult, more effective strategies should be implemented. Faculty members from several Italian Scientific Societies, Patient Associations and Regional Institutional Experts on RDs met to discuss aHUS management within a multidisciplinary team (MDT), using a Delphi process to develop consensus recommendations. Consensus (≥70 % agreement by faculty members) was reached on 51 statements with the aim of improving patient management and outcomes. These statements provide a unified framework for the differential diagnosis of aHUS, prompt recognition of the pathology, referral to RD reference centers, selecting between treatment relapse prevention measures options, patient management by a MDT and improving the overall awareness of aHUS. Despite the broad scope of the consensus statements, several unmet needs in the management of patients with aHUS were identified, including diagnostic suspicion, rapid genetic investigations, regular review of the centers of expertise (considering the number of treated patients), permanent clinical referral in treatment centers and widespread expertise among adult and pediatric specialists. We hope that this standardized framework will form the basis of the "digital ecosystem" concept and development of possible information technology solutions to assist the MDT involved in the management of patients with aHUS.
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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 | 0.110 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".