A multimodal approach to detect and monitor early lung disease in cystic fibrosis
Bibliographic record
Abstract
Introduction: In the early stages, lung involvement in cystic fibrosis (CF) can be silent, with disease progression occurring in the absence of clinical symptoms. Irreversible airway damage is present in the early stages of disease; however, reliable biomarkers of early damage due to inflammation and infection that are universally applicable in day-to-day patient management have yet to be identified.Areas covered: At present, the main methods of detecting and monitoring early lung disease in CF are the lung clearance index (LCI), computed tomography (CT), and magnetic resonance imaging (MRI). LCI can be used to detect patients who may require more intense monitoring, identify exacerbations, and monitor responses to new interventions. High-resolution CT detects structural alterations in the lungs of CF patients with the best resolution of current imaging techniques. MRI is a radiation-free imaging alternative that provides both morphological and functional information. The role of MRI for short-term follow-up and pulmonary exacerbations is currently being investigated.Expert opinion: The roles of LCI and MRI are expected to expand considerably over the next few years. Meanwhile, closer collaboration between pulmonology and radiology specialties is an important goal toward improving care and optimizing outcomes in young patients with CF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".