Breath of fresh insight: unraveling the evolution of our understanding of cystic fibrosis pulmonary exacerbations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pulmonary exacerbations are critical events with significant negative impacts in persons with cystic fibrosis, but their diagnosis and management are highly variable. Highly effective modulator therapies have greatly improved health and reduced exacerbation events, but have also reshaped how they present. This review discusses the complexities of the diagnosis and management of pulmonary exacerbations as well as the emerging work and evidence in this area. RECENT FINDINGS: The shifting epidemiology and our understanding of risk factors for pulmonary exacerbations are discussed. As symptoms may be more subtle in the modulator context, novel technologies including studies of remote monitoring are presented. The continued relevance of pulmonary exacerbations, the heterogeneity in their management, as well as current and forthcoming clinical trials to optimize treatment approaches are detailed. SUMMARY: In spite of the dramatic reductions in pulmonary exacerbations, airway infections persist, a proportion of persons with cystic fibrosis either on or off modulator therapies continue to experience exacerbation events, and long-term data is lacking. Innovative approaches and studies will be crucial to enable standardized and generalizable strategies to improve outcomes in persons with cystic fibrosis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".