Antibiotic Management of Lung Infections in Cystic Fibrosis. I. The Microbiome, Methicillin-Resistant <i>Staphylococcus aureus</i> , Gram-Negative Bacteria, and Multiple Infections
Bibliographic record
Abstract
Despite significant advances in treatment strategies targeting the underlying defect in cystic fibrosis (CF), airway infection remains an important cause of lung disease. In this two-part series, we review recent evidence related to the complexity of CF airway infection, explore data suggesting the relevance of individual microbial species, and discuss current and future treatment options. In Part I, the evidence with respect to the spectrum of bacteria present in the CF airway, known as the lung microbiome is discussed. Subsequently, the current approach to treat methicillin-resistant Staphylococcus aureus, gram-negative bacteria, as well as multiple coinfections is reviewed. Newer molecular techniques have demonstrated that the airway microbiome consists of a large number of microbes, and the balance between microbes, rather than the mere presence of a single species, may be relevant for disease pathophysiology. A better understanding of this complex environment could help define optimal treatment regimens that target pathogens without affecting others. Although relevance of these organisms is unclear, the pathologic consequences of methicillin-resistant S. aureus infection in patients with CF have been recently determined. New strategies for eradication and treatment of both acute and chronic infections are discussed. Pseudomonas aeruginosa plays a prominent role in CF lung disease, but many other nonfermenting gram-negative bacteria are also found in the CF airway. Many new inhaled antibiotics specifically targeting P. aeruginosa have become available with the hope that they will improve the quality of life for patients. Part I concludes with a discussion of how best to treat patients with multiple coinfections.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".