Impact of comorbidities on risk of angioedema without urticaria in elderly patients
Bibliographic record
Abstract
BACKGROUND: Angioedema without urticaria (AWU) is a disease found in the elderly population but is still poorly studied. The aim of this study was to investigate potential factors, especially comorbidities, that may affect the induction of angioedema without urticaria in patients over 60 years of age. METHODS: This was an observational, retrospective study of 242 patients with a diagnosis of AWU and 263 controls. The inclusion criteria were as follows: at least one episode of confirmed AWU based on the ICD-10 code (T78.3) that required treatment in the last 15 years (2004-2019); age above 60 years; detailed medical history of comorbidities; and details regarding the use of drugs at that time. Serum functional and quantitative C1 inhibitor assays were performed, and serum C4 was measured. Comorbidities were grouped into the following panels: autoimmune, cancer, cardiac, metabolic, respiratory and allergic, liver failure and renal failure. Individual diseases were checked according to ICD code and treatment. RESULTS: In 1 (0.4%) patient, hereditary angioedema was confirmed. Decreased levels of C1INH were observed in 4 (1.65%) patients, dysfunction of C1INH was observed in 5 (1.76%) patients, and low levels of C4 were observed in 9 (3.71%) patients in the study group. The multiple logistic regression model revealed that patients with hyperuricemia or Hashimoto's disease had a significantly higher chance of angioedema (OR = 3.21, 95% CI 2.92-3.66, p = 0.002; OR = 1.78 95% CI 1.37-2.21, p = 0.034, respectively). CONCLUSION: The obtained results may indicate a significant influence of hyperuricemia or Hashimoto's disease on angioedema manifestations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".