Hereditary or acquired? Comprehensive genetic testing assists in stratifying angioedema patients
Bibliographic record
Abstract
Hereditary angioedema (HAE) poses diagnostic challenges due to its episodic, non-specific symptoms and overlapping conditions. This study focuses on the genetic basis of HAE, particularly focusing on unresolved cases and those with normal C1-inhibitor levels (nC1-INH HAE). This study reveals that conventional testing identified pathogenic variants in only 10 patients (n = 32), emphasizing the necessity for an integrative approach using genome, exome, and transcriptome sequencing. Despite extensive genetic analyses, the diagnostic yield for nC1-INH HAE remains low in our study, the pathogenic variant for nC1-INH HAE was identified in only 1 patient (n = 21). Investigation into candidate genes yielded no pathogenic variants, prompting a re-evaluation of patients' diagnoses. This study advocates for a nuanced approach to genetic testing, recognizing its limitations and emphasizing the need for continuous clinical assessment. The complex genetic landscape of nC1-INH HAE necessitates further research for a more comprehensive understanding. In conclusion, this study contributes valuable insights into the genetic intricacies of HAE, highlighting the challenges in diagnosis and the evolving nature of the disease. The findings underscore the importance of advanced sequencing techniques and an integrated diagnostic strategy in unravelling the complexities of HAE, particularly in nС1-INH HAE cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".