Clinical manifestations in primary ciliary dyskinesia: systematic review and meta-analysis
Bibliographic record
Abstract
Few original studies have described the prevalence and severity of clinical symptoms of primary ciliary dyskinesia (PCD). This systematic review and meta-analysis aimed to identify all published studies on clinical manifestations of PCD patients, and to describe their prevalence and severity stratified by age and sex. We searched PubMed, Embase and Scopus for studies describing clinical symptoms of ≥10 patients with PCD. We performed meta-analyses and meta-regression to explain heterogeneity. We included 52 studies describing a total of 1970 patients (range 10–168 per study). We found a prevalence of 5% for congenital heart disease. For the rest of reported characteristics, we found considerable heterogeneity (I 2 range 68–93.8%) when calculating the weighted mean prevalence. Even after taking into account the explanatory factors, the largest part of the between-studies variance in symptom prevalence remained unexplained for all symptoms. Sensitivity analysis including only studies with test-proven diagnosis showed similar results in prevalence and heterogeneity. Large differences in study design, selection of study populations and definition of symptoms could explain the heterogeneity in symptom prevalence. To better characterise the disease, we need larger, multicentre, multidisciplinary, prospective studies that include all age groups, use uniform diagnostics and report on all symptoms.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".