Estimates of primary ciliary dyskinesia prevalence: a scoping review
Bibliographic record
Abstract
Background: Primary ciliary dyskinesia (PCD) is a rare multisystem genetic disease caused by dysfunctional motile cilia. Despite PCD being the second most common inherited airway disease after cystic fibrosis, PCD continues to be under-recognised globally owing to nonspecific clinical features and the lack of a gold standard diagnostic test. Commonly repeated prevalence estimates range from one in 10 000 to one in 20 000, based on regional epidemiological studies with known limitations. The purpose of this scoping review was to appraise the PCD literature, to determine the best available global PCD prevalence estimate and to inform the reader about the potential unmet health service needs in PCD. The primary objective of the present study was to systematically review the literature about PCD prevalence estimates. Methods: A scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) methodology. Included studies estimated PCD prevalence and used cohort, clinical or genomic data. Case reports, conference abstracts, review articles, animal studies or non-English articles were excluded. Results: A literature review identified 3484 unique abstracts; 34 underwent full-text review and eight met the inclusion/exclusion criteria. Seven articles were based on epidemiological studies of specific geographical regions and provided prevalence estimates that ranged from approximately one to 44.1 in 100 000. Only one study estimated global prevalence, using two large genomic databases, and calculated it to be ∼13.2 in 100 000 (based on pathogenic variants in 29 disease-causing genes). Conclusions: A population-based genomic approach for estimating global prevalence has found that PCD is much more prevalent than previously cited in the literature. This highlights the potential unmet health service needs of people living with 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 | 0.034 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.046 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".