Primary ciliary dyskinesia: clinical manifestations and current diagnostic approaches
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the clinical symptoms of primary ciliary dyskinesia (PCD) beginning at birth and current approaches for confirming diagnosis. Strengths and limitations of innovative adjunctive tests to improve detection are discussed, ultimately highlighting the importance of PCD expert networks to develop standardized guidelines and develop a standalone diagnostic tool. RECENT FINDINGS: PCD is underdiagnosed globally, reflecting overall awareness of this disease and limitations of diagnostic approaches. Over 50 disease-causing genes have been characterized, yet more are discovered each year. No single test can detect all PCD cases, therefore further research is needed to improve clinical options for diagnosis. SUMMARY: PCD is a genetic ciliopathy with serious health complications and impacts on quality of life. Clinical manifestation can vary significantly between individuals, which can delay diagnosis and negatively affect patient outcomes. Current diagnostic tests for PCD require significant resources and training to interpret, and the best-available tests may miss up to 30% of cases. Further work facilitated by expert collaborative networks will be instrumental to develop novel, enhanced diagnostic tools and ultimately improve outcomes for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".