Whole exome sequencing enhances diagnosis of hereditary bronchiectasis
Bibliographic record
Abstract
BACKGROUND: Hereditary bronchiectasis refers to a subset of bronchiectasis related to genetic mutations, presenting with common clinical features. Historically, diagnosing this condition has been difficult due to the inaccessibility of diagnostic services coupled with a lack of awareness of the syndrome. We hypothesize that whole exome sequencing (WES) in patients with supporting clinical features, combined with non-genetic testing methods, will enhance the diagnosis of hereditary bronchiectasis. RESULTS: In total, 87 patients with clinical features suggestive of hereditary bronchiectasis, such as diffuse bronchiectasis (≥ 2 lobes) combined with early onset symptoms, recurrent otitis media, rhinosinusitis, infertility, organ laterality defects or a family history of bronchiectasis, were included in this study. Among them, 49.4% (43/87) were diagnosed with hereditary bronchiectasis, including 15 patients with cystic fibrosis, 27 patients with primary ciliary dyskinesia, and 1 patient with immunodeficiency-21. The combined use of WES and non-genetic testing methods significantly improved the diagnostic rate of hereditary bronchiectasis compared to non-genetic testing alone (47.1% vs. 25.3%, P = 0.005). Re-analysis of negative commercial genetic tests led to two additional diagnoses, though this increase was not statistically significant (47.1% vs. 49.4%, P = 0.879). CONCLUSIONS: We have described the supporting clinical features of patients with hereditary bronchiectasis. Clinicians should recommend WES for patients exhibiting these characteristics, in combination with accessible non-genetic testing methods, to maximize diagnostic accuracy. For patients with negative initial genetic test results, re-analysis of WES data may facilitate obtaining a new diagnosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".