Special considerations for the treatment of pulmonary exacerbations in children with cystic fibrosis
Bibliographic record
Abstract
INTRODUCTION: Cystic fibrosis (CF) is a disease characterized by recurrent flares of respiratory symptoms, known as pulmonary exacerbations (PExs), which have a cumulative, detrimental effect on lung function decline and overall mortality. Although much research has been done on the effects of PExs in adults with CF, considerably less is known about these events in young children with CF. Areas covered: This review describes the typical presentation of PExs in children and their impact on long-term clinical outcomes. Traditional and new monitoring techniques, such as pulmonary function testing using multiple breath washout, radiographic modalities and microbiological screening methods are reviewed. Finally, the choice, administration and duration of antimicrobial treatment as well as the potential use of antiviral therapy is discussed. Expert commentary: Although it is now well recognized that a significant proportion of patients do not recover their lung function following PExs, to date, little progress has been made to improve outcomes in this group. Additional therapies, to complement antimicrobials, may be required to treat infection and inflammation during PExs. Trials of anti-inflammatories such as corticosteroids or other novel drugs need to be done in the setting of PExs with the goal of complete lung function recovery for all individuals 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".