Disease control and disease activity in hereditary angioedema: two sides of the same coin?
Bibliographic record
Abstract
Background: Hereditary angioedema (HAE) is a genetic disorder characterized by episodic subcutaneous and submucosal swelling. Patient-reported outcome measures (PROMs) are recommended for routine clinical assessment by international guidelines and are used as secondary outcome measures in pivotal clinical trials for novel investigational drugs. The Angioedema Control Test (AECT) and Hereditary Angioedema Activity Score (HAE-AS) are validated tools designed to aid in HAE patient assessment, but the extent to which they measure different disease outcomes is unclear. The aim of this study was to examine how these measures correlate and inform clinical practice. Methods: We conducted a retrospective study of patients with HAE types 1 and 2 at the Edmonton Angioedema Center of Reference and Excellence. AECT and HAE-AS scores were obtained from patient-completed questionnaires during routine visits. Multiple linear regression was used to examine the association between HAE-AS and AECT scores with age, sex, and long-term prophylaxis (LTP) status as predictors. Receiver operating characteristic (ROC) analysis was used to determine the optimal HAE-AS score cutoff that predicts poor disease control as determined by the AECT. Results: There were 25 participants included with a mean age of 39.4 years (SD = 13.7), 72% of whom were female. Most had HAE Type 1 (76%) and 52% were receiving LTP. SC C1-inhibitor therapy was the most common LTP (36%). Most had well managed disease with a median AECT score of 11.88 (range: 5-16) and HAE-AS of 5.84 (range: 0-13). A statistically significant but weak negative correlation was found between AECT and HAE-AS (β=-0.67, p=0.002). ROC analysis showed that an HAE-AS score >5 had a sensitivity of 100% and specificity of 61% for poor disease control. Conclusions: The AECT and HAE-AS instruments are weakly correlated, indicating that they provide related but distinct information to the practicing clinician. Using both AECT and HAE-AS in clinical practice can thus provide a more comprehensive patient evaluation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".