Lung ultrasound in children with primary ciliary dyskinesia or cystic fibrosis
Bibliographic record
Abstract
INTRODUCTION: Primary ciliary dyskinesia (PCD) and cystic fibrosis (CF) are respiratory conditions requiring regular chest radiography (CXR) surveillance to monitor pulmonary disease. However, CXR is insensitive for lung disease in CF and PCD. Lung ultrasound (LU) is a radiation-free alternative showing good correlation with severity of lung disease in CF but has not been studied in PCD. METHOD: Standardized, six-zone LU studies and CXR were performed on a convenience sample of children with PCD or CF during a single visit when well. LU studies were graded using the LU scoring system, while CXR studies received a modified Chrispin-Norman score. Scores were correlated with clinical outcomes. RESULT: 100%) were analyzed. LU abnormalities appear in 11/30 (36%) patients with PCD and 9/30 (30%) with CF. Sensitivity, specificity, positive predictive, and negative predictive values for abnormal LU compared to the gold standard of CXR are 42%, 61%, 42%, and 61% in PCD, and 44%, 81%, 50%, and 77% in CF, respectively. Correlation between LU and CXR scores are poor for both diseases (PCD r = -0.1288, p = 0.4977; CF r = 0.0343, p = 0.8571), and LU score does not correlate with clinical outcomes in PCD. CONCLUSION: The correlation of LU findings with CXR surveillance studies is poor in patients with mild disease burdens from PCD or CF, and LU scores do not correlate with clinical outcomes in 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".