Late Breaking Abstract - Clinical and microbiological impacts of long-term modulator therapy in cystic fibrosis: a registry-based analysis
Bibliographic record
Abstract
Background: As modulators become lifelong therapy for cystic fibrosis (CF), including increasingly younger patients, their long-term effects on pulmonary function and airway microbiology remain uncharacterized. While modulators improve clinical outcomes, persistent colonization with pathogens and structural lung damage continue to be reported, often resembling age-matched controls. This raises critical questions about the disease progression in the modulator era— particularly given limited longitudinal data Aims: This study evaluates the long-term clinical and microbial impacts of CF modulators through a registry-based cohort analysis using data from the Canadian and U.S. CF registries (2019–2023). We focus on patients with >12 months of continuous modulator use. Primary outcomes include lung function (FEV₁) changes and trends in key pathogens: P. aeruginosa, S. maltophilia, and nontuberculous mycobacteria (NTM). Results: Initial analysis shows a notable rise in reported NTM detections in non-hospitalized adults on modulators—from 22 to 88 cases from 2019 to 2023. While this 300% relative increase will be adjusted in final models, it aligns with emerging concerns that microbial adaptation may persist despite clinical gains. Ongoing analysis will examine duration-dependent microbial trends, FEV₁ sustainability, and predictors of persistent or emerging infections. To our knowledge, this is the first multinational registry study to longitudinally track pathogen-specific trends in long-term modulator use. Results will provide urgent insights into microbial surveillance, infection risk stratification, and long-term evolution of CF airway disease in the global modulator era.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".