Primary Ciliary Dyskinesia
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is a rare, genetic disease characterized by dysfunctional motile cilia and abnormal mucociliary clearance, resulting in chronic sino-oto-pulmonary disease, neonatal respiratory distress, subfertility, and organ laterality defects. Over the past 2 decades, research and international collaborations have led to an improved understanding of disease prevalence, classic and variable phenotypes, novel diagnostics, genotype-phenotype correlations, long term morbidity, and innovative therapeutics. However, PCD is often underrecognized in clinical settings and the recent analyses of genetic databases suggest that only a fraction of these patients are being accurately diagnosed. Knowledge of significant advancements, from pathophysiology to the expanded range of clinical manifestations, will have important clinical impacts. These may include increasing disease recognition, improving diagnostic testing and management, and establishing an adequate pool of affected patients to enroll in upcoming clinical therapeutic trials. The objective of this state-of-the-art review is for readers to gain a greater understanding of the clinical spectrum of motile ciliopathies, cutting-edge diagnostic practices, emerging genotype-phenotype associations, and currently accepted management of people with PCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".