Standardizing Nasal Nitric Oxide Measurement as a Test for Primary Ciliary Dyskinesia
Bibliographic record
Abstract
RATIONALE: Several studies suggest that nasal nitric oxide (nNO) measurement could be a test for primary ciliary dyskinesia (PCD), but the procedure and interpretation have not been standardized. OBJECTIVES: To use a standard protocol for measuring nNO to establish a disease-specific cutoff value at one site, and then validate at six other sites. METHODS: At the lead site, nNO was prospectively measured in individuals later confirmed to have PCD by ciliary ultrastructural defects (n = 143) or DNAH11 mutations (n = 6); and in 78 healthy and 146 disease control subjects, including individuals with asthma (n = 37), cystic fibrosis (n = 77), and chronic obstructive pulmonary disease (n = 32). A disease-specific cutoff value was determined, using generalized estimating equations (GEEs). Six other sites prospectively measured nNO in 155 consecutive individuals enrolled for evaluation for possible PCD. MEASUREMENTS AND MAIN RESULTS: At the lead site, nNO values in PCD (mean ± standard deviation, 20.7 ± 24.1 nl/min; range, 1.5-207.3 nl/min) only rarely overlapped with the nNO values of healthy control subjects (304.6 ± 118.8; 125.5-867.0 nl/min), asthma (267.8 ± 103.2; 125.0-589.7 nl/min), or chronic obstructive pulmonary disease (223.7 ± 87.1; 109.7-449.1 nl/min); however, there was overlap with cystic fibrosis (134.0 ± 73.5; 15.6-386.1 nl/min). The disease-specific nNO cutoff value was defined at 77 nl/minute (sensitivity, 0.98; specificity, >0.999). At six other sites, this cutoff identified 70 of the 71 (98.6%) participants with confirmed PCD. CONCLUSIONS: Using a standardized protocol in multicenter studies, nNO measurement accurately identifies individuals with PCD, and supports its usefulness as a test to support the clinical diagnosis of PCD.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".