Prospective evaluation of respiratory exacerbations in children with cystic fibrosis from newborn screening to 5 years of age
Bibliographic record
Abstract
BACKGROUND: Newborn screening allows novel treatments for cystic fibrosis (CF) to be trialled in early childhood before irreversible lung injury occurs. As respiratory exacerbations are a potential trial outcome variable, we determined their rate, duration and clinical features in preschool children with CF; and whether they were associated with growth, lung structure and function at age 5 years. METHODS: Respiratory exacerbations were recorded prospectively in Australasian CF Bronchoalveolar Lavage trial subjects from enrolment after newborn screening to age 5 years, when all participants underwent clinical assessment, chest CT scans and spirometry. RESULTS: 168 children (88 boys) experienced 2080 exacerbations, at an average rate of 3.66 exacerbations per person-year; 80.1% were community managed and 19.9% required hospital admission. There was an average increase in exacerbation rate of 9% (95% CI 4% to 14%; p<0.001) per year of age. Exacerbation rate differed by site (p<0.001) and was 26% lower (95% CI 12% to 38%) in children receiving 12 months of prophylactic antibiotics. The rate of exacerbations in the first 2 years was associated with reduced forced expiratory volume in 1 s z scores. Ever having a hospital-managed exacerbation was associated with bronchiectasis (OR 2.67, 95% CI 1.13 to 6.31) in chest CT scans, and lower weight z scores at 5 years of age (coefficient -0.39, 95% CI -0.74 to -0.05). CONCLUSIONS: Respiratory exacerbations in young children are markers for progressive CF lung disease and are potential trial outcome measures for novel treatments in this age group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".