The international WAO/EAACI guideline for the management of hereditary angioedema – The 2021 revision and update
Bibliographic record
Abstract
Hereditary Angioedema (HAE) is a rare and disabling disease for which early diagnosis and effective therapy are critical. This revision and update of the global WAO/EAACI guideline on the diagnosis and management of HAE provides up-to-date guidance for the management of HAE. For this update and revision of the guideline, an international panel of experts reviewed the existing evidence, developed 28 recommendations, and established consensus by an online DELPHI process. The goal of these recommendations and guideline is to help physicians and their patients in making rational decisions in the management of HAE with deficient C1-inhibitor (type 1) and HAE with dysfunctional C1-inhibitor (type 2), by providing guidance on common and important clinical issues, such as: 1) How should HAE be diagnosed? 2) When should HAE patients receive prophylactic on top of on-demand treatment and what treatments should be used? 3) What are the goals of treatment? 4) Should HAE management be different for special HAE patient groups such as children or pregnant/breast feeding women? 5) How should HAE patients monitor their disease activity, impact, and control? It is also the intention of this guideline to help establish global standards for the management of HAE and to encourage and facilitate the use of recommended diagnostics and therapies for all patients.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".