ENHANCE: a community partnership charting the evolution of early-life cystic fibrosis disease manifestations in a new era of management
Bibliographic record
Abstract
ENHANCE is a multicentre, longitudinal study examining the natural history of early cystic fibrosis (CF) disease manifestations in children. We hypothesise that the prevalence, presentation and natural history of disease manifestations of CF in young children will change significantly in the next decade with advances in the understanding and treatment of CF, including the use of therapies aimed at improving CFTR function. The study has been co-developed with the CF community, in order to try to answer questions of key importance to people with CF and their caregivers. The study will longitudinally monitor lung health using nitrogen multiple breath washout testing and spirometry, lung structure using spirometry-controlled computed tomography scanning, gastrointestinal health using gut microbiome, gut inflammation and abdominal symptom scores, pancreatic function using faecal elastase, liver function using liver blood tests and liver ultrasound, and sweat chloride. Mental health will be longitudinally assessed using questionnaires for mood (PROMIS, GAD-7), quality of life (CFQ-R) and psychosocial functioning (Pediatric Symptom Checklist). There are three cohorts in the study: infants with CF diagnosed by newborn screening (n=250), children aged 0-6 years and currently attending CF centres (n=200) and healthy newborn infant controls (100). The study will be conducted over 5 years in the first phase with a plan to extend through further phases in the future. We envisage that the study will provide important evidence to enable healthcare staff and families to guide the care of children with CF in the future.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".