New data from the Italian National Register of Congenital Coagulopathies, 2016 Annual Survey.
Bibliographic record
Abstract
BACKGROUND: In Italy, the National Register of Congenital Coagulopathies (NRCC) collects epidemiological and therapeutic data from patients affected by haemophilia A (HA), haemophilia B (HB), von Willebrand's disease (vWD) and other rare coagulation disorders. Here we present data from the 2016 annual survey. MATERIALS AND METHODS: Data are provided by the Italian Haemophilia Centres, on a voluntary basis. Information flows from every Centre to a web-based platform of the Italian Association of Haemophilia Centres, shared with the Italian National Institute of Health, in accordance with current privacy laws. Patients are classified by diagnosis, disease severity, age, gender and treatment-related complications. RESULTS: In 2016, the total number of patients with congenital coagulopathies in the NRCC was 10,360: 39.8% of these patients had HA, 31.5% had vWD, 8.5% had HB, and 20.2% had less common factor deficiencies. The overall prevalence of HA and HB was 13.9/100,000 males and 3.0/100,000 males, respectively. The overall prevalence of vWD was 5.4/100,000 inhabitants. During 2016, 126 patients had current alloantibodies to factor VIII (FVIII) or factor IX (FIX) and were under treatment with bypassing agents and/or immune tolerance induction. Overall, 388 patients with a history of alloantibodies were recorded in the NRCC of whom 337 with severe HA and 12 with severe HB. Coagulation factor use, evaluated from treatment plans, was approximately 451,000,000 IU of FVIII for HA patients (7.5 IU/inhabitant), and approximately 53,000,000 IU of FIX for HB patients (0.9 IU/inhabitant). DISCUSSION: The prevalences of HA and HB fall within the ranges reported in more developed countries; the consumption of FVIII and FIX was in line with that of other European countries (France, United Kingdom) and Canada. The NRCC, with its bleeding disorder dataset, is a helpful tool for shaping public health policies, as well as planning clinical and epidemiological research projects.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".