Lung function in infants with cystic fibrosis diagnosed by newborn screening
Bibliographic record
Abstract
RATIONALE: Progressive lung damage in cystic fibrosis (CF) starts in infancy, and early detection may aid preventative strategies. OBJECTIVES: To measure lung function in infants with CF diagnosed by newborn screening and describe its association with pulmonary infection and inflammation. METHODS: Infants with CF (n = 68, 6 weeks to 30 months of age) and healthy infants without CF (n = 49) were studied. Forced vital capacity, FEV(0.5), and forced expiratory flows at 75% of exhaled vital capacity (FEF(75)) were measured using the raised-volume rapid thoracoabdominal compression technique. Forty-eight hours later, infants with CF had bronchoalveolar lavage (BAL) for assessment of pulmonary infection and inflammation. MEASUREMENTS AND MAIN RESULTS: In the CF group, the deficit in FEV(0.5) z score increased by -0.77 (95% confidence interval, -1.14 to -0.41; P < 0.001) with each year of age. The mean FEV(0.5) z score did not differ between infants with CF and healthy control subjects less than 6 months of age (-0.06 and 0.02, respectively; P = 0.87). However, the mean FEV(0.5) z score was lower by 1.15 in infants with CF who were older than 6 months of age compared with healthy infants (P < 0.001). FVC and FEF(75) followed a similar pattern. Pulmonary infection and inflammation in BAL samples did not explain the lung function results. CONCLUSIONS: Lung function, measured by forced expiration, is normal in infants with CF at the time of diagnosis by newborn screening but is diminished in older infants. These findings suggest that in CF the optimal timing of therapeutic interventions aimed at preserving lung function may be within the first 6 months of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".