Management of hereditary angioedema attacks by patients on long-term prophylaxis versus on-demand therapy only
Bibliographic record
Abstract
Background: Despite the use of long-term prophylaxis (LTP) for hereditary angioedema (HAE), the risk of having an attack remains and patients with HAE and on LTP may still experience attacks that can be life threatening. However, the behavioral patterns and perspectives surrounding HAE attack management by patients on LTP are not fully understood. Objective: This survey aimed to better understand and compare the behavioral patterns and perspectives, including attitudes and perceptions associated with on-demand treatment among patients on LTP versus those using on-demand therapy only. Methods: People living with HAE were recruited by the US Hereditary Angioedema Association to complete a 20-minute online survey between September 6 and October 19, 2022. Participants were stratified by treatment (50% using LTP [+on-demand therapy], 50% on-demand therapy only). Results: Respondents included 107 patients with HAE (mean age, 41 years [range, 16‐83 years]). Patients using LTP reported treating a mean ± standard deviation 84.8% ± 23.8% of their HAE attacks compared with a mean ± standard deviation 75.6% ± 27.5% for patients with on-demand only treatment. Similar percentages of patients on LTP versus patients on-demand only reported always carrying on-demand treatment when away from home (35% versus 38%) and modifying their daily lives to minimize the occurrence of HAE attacks, which included avoiding potential triggers (42.9% versus 45.5%). Conclusion: Although patients on LTP treat a higher percentage of their attacks compared with patients with on-demand only treatment, both groups reported similar behaviors in terms of carrying on-demand treatment when away from home and modifying their daily lives to minimize the occurrence of HAE attacks. These findings highlight the importance of understanding patient perspectives and behaviors in the management of HAE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".