Quality of life and burden of disease in patients with hereditary angioedema and their caregivers
Bibliographic record
Abstract
Background: Hereditary angioedema (HAE) substantially impairs patients’ quality of life (QoL), both physically and psychologically, with unpredictable attacks that cause disruptions in education, work, and social life. Objective: To identify key themes and existing knowledge gaps around the multifaceted burden of HAE. Methods: A literature review was conducted in January 2024 through a search of medical literature data bases. English-language studies considered relevant to patient burden and QoL were selected for analysis. Results: A total of 48 studies were included in the analysis; 50% were cross-sectional and 54% were conducted in North America. Twenty-three studies reported outcomes on QoL and pain, 10 studies reported outcomes on psychological distress, 16 studies reported outcomes on experiences with long-term prophylaxis, 36 studies reported outcomes on HAE attacks, and one study detailed caregiver burden. Patients with HAE had worse QoL compared with the general population, and worse QoL was associated with a higher frequency or severity of attacks, anxiety, and depression. The use of long-term prophylaxis improved QoL, and treatment satisfaction was driven by improvements in mental health and fostering a sense of control and independence. Conclusion: HAE continues to substantially impact QoL of patients. Although recent work has demonstrated progress in standardizing assessment tools for QoL in HAE, additional research is needed to determine the correlation between individual patient factors and QoL.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".