Outcomes of Cystic Fibrosis Screening–Positive Infants With Inconclusive Diagnosis at School Age
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cystic fibrosis (CF) screen-positive infants with an inconclusive diagnosis (CFSPID) are infants in whom sweat testing and genetic analysis does not resolve a CF diagnosis. Lack of knowledge about the health outcome of these children who require clinical follow-up challenges effective consultation. Early predictive biomarkers to delineate the CF risk would allow a more targeted approach to these children. METHODS: Prospective, longitudinal, multicenter, Canada-wide cohort study of CF positive-screened newborns with 1 to 2 cystic fibrosis transmembrane conductance regulator gene variants, of which at least 1 is not known to be CF-causing and/or a sweat chloride between 30 and 59 mmol/L. These were monitored for conversion to a CF diagnosis, pulmonary, and nutritional outcomes. RESULTS: The mean observation period was 7.7 (95% confidence interval 7.1 to 8.4) years. A CF diagnosis was established for 24 of the 115 children with CFSPID (21%) either because of reinterpretation of the cystic fibrosis transmembrane conductance regulator genotype or because of increase in sweat chloride concentration ≥60 mmol/L. An initial sweat chloride of ≥40 mmol/l predicted conversion to CF on the basis of sweat testing. The 91 remaining children with CFSPID were pancreatic sufficient and showed normal growth until school age. Pulmonary function as well as lung clearance index in a subgroup of children with CFSPID were similar to that of healthy controls. CONCLUSIONS: Children with CFSPID have good nutritional and pulmonary outcomes at school age, but rates of reclassifying the diagnosis are high. The initial sweat chloride test can be used as a biomarker to predict the risk for CF in CFSPID.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".