Primary Ciliary Dyskinesia. Recent Advances in Diagnostics, Genetics, and Characterization of Clinical Disease
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is a genetically heterogeneous recessive disorder of motile cilia that leads to oto-sino-pulmonary diseases and organ laterality defects in approximately 50% of cases. The estimated incidence of PCD is approximately 1 per 15,000 births, but the prevalence of PCD is difficult to determine, primarily because of limitations in diagnostic methods that focus on testing ciliary ultrastructure and function. Diagnostic capabilities have recently benefitted from (1) documentation of low nasal nitric oxide production in PCD and (2) discovery of biallelic mutations in multiple PCD-causing genes. The use of these complementary diagnostic approaches shows that at least 30% of patients with PCD have normal ciliary ultrastructure. More accurate identification of patients with PCD has also allowed definition of a strong clinical phenotype, which includes neonatal respiratory distress in >80% of cases, daily nasal congestion and wet cough starting soon after birth, and early development of recurrent/chronic middle-ear and sinus disease. Recent studies, using advanced imaging and pulmonary physiologic assessments, clearly demonstrate early onset of lung disease in PCD, with abnormal air flow mechanics by age 6-8 years that is similar to cystic fibrosis, and age-dependent onset of bronchiectasis. The treatment of PCD is not standardized, and there are no validated PCD-specific therapies. Most patients with PCD receive suboptimal management, which should include airway clearance, regular surveillance of pulmonary function and respiratory microbiology, and use of antibiotics targeted to pathogens. The PCD Foundation is developing a network of clinical centers, which should improve diagnosis and management of PCD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".