Optimal Biopsy Techniques in the Diagnosis of Primary Ciliary Dyskinesia
Bibliographic record
Abstract
OBJECTIVE: Adequate biopsy specimens that clearly demonstrate cilia, and therefore enable the determination of the presence or absence of primary cilia dyskinesia, may be difficult to obtain. This study is an attempt to identify the optimal sampling technique to best examine respiratory tract cilia. DESIGN: A prospective comparison of the four sampling techniques was carried out: nasal brushing, nasal biopsy, bronchial brushing, and tracheal biopsy. SETTING: Tertiary care pediatric hospital: Children's Hospital of Eastern Ontario. METHODS: Ten consecutive patients booked for bronchoscopy and tracheal biopsy underwent all four procedures. Specimens were examined under light microscopy for an assessment of quality. RESULTS: The nasal brushing and tracheal biopsy specimens provide superior quality (p = .22); however, nasal brushing is more cost efficient. Nasal biopsy samples frequently are metaplastic and therefore are inferior to nasal brushing samples (p = .02). CONCLUSION: With equal efficiency demonstrated, the reduction in potential morbidity and health care costs suggests nasal brushings to be the optimal initial investigation for primary ciliary dyskinesia.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".