Diagnostic yield of nasal scrape biopsies in primary ciliary dyskinesia: A multicenter experience
Bibliographic record
Abstract
Examination of ciliary ultrastructure remains the cornerstone diagnostic test for primary ciliary dyskinesia (PCD), a disease of abnormal ciliary structure and/or function. Obtaining a biopsy with sufficient interpretable cilia and producing quality transmission electron micrographs (TEM) is challenging. Methods for processing tissues for optimal preservation of axonemal structures are not standardized. This study describes our experience using a standard operating procedure (SOP) for collecting nasal scrape biopsies and processing TEMs in a centralized laboratory. We enrolled patients with suspected PCD at research sites of the Genetic Disorders of Mucociliary Clearance Consortium. Biopsies were performed according to a SOP whereby curettes were used to scrape the inferior surface of the inferior turbinate, with samples placed in fixative. Specimens were shipped to a central laboratory where TEMs were prepared and blindly reviewed. Four hundred forty-eight specimens were obtained from 107 young children (0-5 years), 189 older children (5-18 years), and 152 adults (> 18 years), and 88% were adequate for formal interpretation. The proportion of adequate specimens was higher in adults than in children. Fifty percent of the adequate TEMs showed normal ciliary ultrastructure, 39% showed hallmark ultrastructural changes of PCD, and 11% had indeterminate findings. Among specimens without clearly normal ultrastructure, 72% had defects of the outer and/or inner dynein arms (IDA), while 7% had central apparatus defects with or without IDA defects. In summary, nasal scrape biopsies can be performed in the outpatient setting and yield interpretable samples, when performed by individuals with adequate training and experience according to an SOP.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".