Primary Ciliary Dyskinesia: An Epidemiological Exploration. Furthering our understanding of the burden of disease in PCD
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is an underrecognized multisystem genetic disorder that is characterized by dysfunctional motile cilia and abnormal mucociliary clearance. In recent years, there have been significant advancements in the understanding of PCD including, but are not limited to, disease frequency estimates in specific ethnic groups and geographical regions, the expansion of diagnostic tests, phenotype/genotype associations and longitudinal lung function. However, PCD continues to be globally underrecognized, in part, due to the heterogeneous and non-specific clinical presentation, and the lack of a gold standard diagnostic test. There are also known limitations with lung function monitoring in PCD, in that spirometry, the most common lung function test, is known to be insensitive to the early stages of lung disease or clinical change. Ultimately, these challenges have impacted the breadth of PCD research relative to other airway diseases, limited available therapeutic options, and decreased interest from industry to conduct interventional trials in this patient population. Therefore, in my dissertation I fill these knowledge gaps by tackling various questions related to the burden of disease in PCD, by employing different clinical epidemiologic techniques.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".