Factors influencing age at diagnosis of primary ciliary dyskinesia in European children
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is a hereditary disorder of mucociliary clearance causing chronic upper and lower airways disease. We determined the number of patients with diagnosed PCD across Europe, described age at diagnosis and determined risk factors for late diagnosis. Centres treating children with PCD in Europe answered questionnaires and provided anonymous patient lists. In total, 223 centres from 26 countries reported 1,009 patients aged < 20 yrs. Reported cases per million children (for 5-14 yr olds) were highest in Cyprus (111), Switzerland (47) and Denmark (46). Overall, 57% were males and 48% had situs inversus. Median age at diagnosis was 5.3 yrs, lower in children with situs inversus (3.5 versus 5.8 yrs; p < 0.001) and in children treated in large centres (4.1 versus 4.8 yrs; p = 0.002). Adjusted age at diagnosis was 5.0 yrs in Western Europe, 4.8 yrs in the British Isles, 5.5 yrs in Northern Europe, 6.8 yrs in Eastern Europe and 6.5 yrs in Southern Europe (p < 0.001). This strongly correlated with general government expenditures on health (p < 0.001). This European survey suggests that PCD in children is under-diagnosed and diagnosed late, particularly in countries with low health expenditures. Prospective studies should assess the impact this delay might have on patient prognosis and on health economic costs across Europe.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".