Interplay between on-demand treatment trials for hereditary angioedema and treatment guidelines
Bibliographic record
Abstract
Over the past 2 decades, guidelines for the on-demand treatment of hereditary angioedema attacks have undergone significant evolution. Early treatment guidelines, such as the Canadian 2003 International Consensus Algorithm, often gated on-demand treatment by attack location and/or severity. Pivotal trials for on-demand injectable treatments (plasma-derived C1 esterase inhibitor, icatibant, ecallantide [United States only], and recombinant human C1 esterase inhibitor), which were approved in the United States and the European Union between 2008 and 2014, were designed accordingly. Subsequent post hoc analyses of clinical trial data alongside real-world evidence led to a paradigm shift. In 2013, the US Hereditary Angioedema Association guidelines recommended that all attacks, irrespective of location or severity, be considered for treatment as early as possible after onset to minimize morbidity and mortality. This approach remains the cornerstone of current treatment guidelines and has shaped the design of recent clinical trials, such as those for the investigational agents, oral plasma kallikrein inhibitor sebetralstat and oral bradykinin B2 receptor antagonist deucrictibant. This narrative review discusses the evolution of on-demand treatment guidelines, the clinical trial and real-world data that prompted significant revisions, and the subsequent changes to trial designs introduced to facilitate guideline compliance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".